Visual and Image Search

Visual search covers the range of ways users can search using images rather than text, or alongside text. This includes Google Images, Google Lens (identifying objects via camera or uploaded image), Circle to Search (an Android overlay feature), and multimodal queries that combine image and text input. Each surface works differently, but they share a common requirement: Google needs to understand what an image depicts, in context, accurately.

This page focuses on the visual retrieval layer. Standard image SEO (file formats, compression, lazy loading, alt text basics) is covered in image SEO. The focus here is on how Google interprets images for discovery, identification, and retrieval across its visual surfaces.

Google Images

Google Images is a vertical search engine for image retrieval. It is a significant traffic source for sites with strong visual content, particularly e-commerce, recipe, design, travel, and news photography sites.

Google’s image SEO guidance names what it uses to understand and surface an image:¹

• Alt text, which Google reads “along with computer vision algorithms and the contents of the page” to work out what the image shows
• Descriptive file names, page titles and the text around the image
• Image quality: sharp images are “more appealing to users in the result thumbnail and can increase the likelihood of getting traffic”
• Structured data on the page (Product, Recipe, Video), which makes an image eligible for a badge or rich result in Google Images

Image clicks in Google Images take users to the page hosting the image, not to the image file directly. The goal is landing page traffic, not image file visibility.

On 14 July 2026, Google Images’ 25th anniversary, Google announced two changes: a browseable home for Google Images, a real-time gallery tailored to a signed-in user’s interests, rolling out on desktop in the US in English; and image generation inside AI Overviews using its Nano Banana model, which turns a text prompt into a custom image made from scratch.² The same post’s look back names the “visual image fan-out” as the technique AI Mode has used since 2025 to break one image search into dozens of sub-queries, and which Circle to Search’s 2026 multi-object recognition applies to several objects in one scene. It is the query fan-out described below, not a separate mechanism in standard image search.

Google Lens

Google Lens is Google’s visual search product. It allows users to search by pointing a camera at something or uploading an image. Lens can:

• Identify objects. Products, plants, animals, landmarks, artwork. It matches visual input against Google’s image index.
• Read text. Recognising text in images, making it searchable or translatable.
• Search similar products. Identifying a product and returning Shopping results for identical or similar items.
• Identify businesses. Pointing at a shop front or building can return the business listing, reviews, and directions.

Lens is integrated into the Google app, Google Images, and Google Search on mobile. It is also available as a standalone camera app on Android.

Circle to Search

Circle to Search is an Android feature (launched January 2024) that allows users to initiate a Google search from within any app, without leaving it. A user can circle, highlight, scribble on, or tap any content visible on screen to trigger a search: text, images, and video frames all work.

From an SEO perspective, Circle to Search extends the surfaces where your content can be discovered. A user watching a YouTube video about interior design can circle a specific lamp and trigger a Google search for that item. The search itself uses Google’s standard systems, so the same ranking and retrieval signals apply.

Multimodal queries

Multimodal search combines image and text input in a single query. A user can photograph a dish and ask “what are the calories in this?”, or point a camera at a plant and ask “is this safe to eat?” The visual context narrows the query in ways text alone cannot.

Google Lens in AI Mode uses Gemini to understand the full scene in an image: the context of how objects relate to each other, their materials, colours, shapes, and arrangements. Rather than treating the image as a single lookup, AI Mode applies a query fan-out technique, issuing multiple queries about the scene as a whole and about individual objects within it, covering more depth than a single text query would.³

The image gives Google context a text query would have to spell out, which is what lets it generate a more specific synthesised answer. Content that surfaces in these results typically covers the identified entity: product, species, location, or concept, with accurate structured data and descriptive prose that matches what visual recognition surfaces.

Video search

Google Lens supports video search by holding the camera shutter to record a moving subject while asking a spoken question. Rather than analysing a single frame, Gemini processes the sequence, capturing motion and context across time. A user at an aquarium can record fish swimming and ask “why are they swimming together?” and receive an AI Overview sourced from relevant pages.⁴

The optimisation requirements are the same as for still images: clearly identifiable subjects, accurate surrounding page context, and structured data. A page covering a species, product, or location that surfaces in a Lens video search needs the same entity clarity as one surfaced by a static image query.

How do you optimise for visual search?

Alt text. The primary text signal Google uses to understand image content. Describe what the image actually shows, concisely and accurately. Do not keyword-stuff; do describe the specific subject, including distinguishing details.

File names. A file named red-ceramic-plant-pot-8cm.jpg is more informative than IMG_2048.jpg. Use descriptive, hyphenated file names that name the subject.

Surrounding context. The text immediately around an image contributes significantly to how Google interprets it. A page about ceramic plant pots where the image appears next to a heading naming the specific product provides strong image context beyond the alt text alone.

Image quality. High-resolution, sharp, well-composed images rank better in visual search than low-quality equivalents. For Lens specifically, the subject must be clearly identifiable: ambiguous, dark, or heavily filtered images are harder to match.

Structured data. Google documents two roles for it in Google Images, and neither is indexing. The image property inside Product, Recipe or Video markup is what makes an image eligible for a badge or rich result in Google Images, and Google calls it a required field for that.¹ ImageObject on its own carries image metadata: the licence, where to license the image, the creator and the credit, which Google Images shows against the image:⁵

{
  "@context": "https://schema.org",
  "@type": "ImageObject",
  "contentUrl": "https://example.com/images/ceramic-plant-pot.jpg",
  "license": "https://example.com/licence",
  "acquireLicensePage": "https://example.com/image-licensing",
  "creditText": "Schema Bean",
  "creator": {
    "@type": "Person",
    "name": "Photographer Name"
  },
  "copyrightNotice": "Schema Bean"
}

For product images, Product schema with a complete image property is what carries the image into Shopping surfaces.

Original images. Stock photography that appears across thousands of sites competes against every other copy for the same visual query. Original images of your specific products, premises, or subjects have no direct visual competitors.

Visual search for products

Product image search is the highest-opportunity area for most commercial sites. A user photographing a product they want to buy can trigger Google Shopping results directly from Lens. To appear in these results:

• Use Product schema with complete image, price, and availability data
• Host high-quality images with multiple angles where possible
• Use clean, well-lit photography with plain backgrounds for product shots
• Ensure the product’s name, brand, and category are clear in surrounding page copy

Google’s surfaces are not the only ones worth assessing here. Pinterest search runs on the same premise from the other direction: users arrive with commercial intent and browse visually rather than typing a sentence, and Pinterest’s ranking now reads colour, style and composition from the image itself. For retail, food, home, fashion and DIY, treat it as a visual search engine to be evaluated alongside Google Images rather than as social media.

How does visual search differ from standard image SEO?

Standard image SEO is primarily about page performance and crawlability: compressing images, using modern formats (WebP, AVIF), implementing lazy loading, and ensuring alt text is present. These are necessary but not sufficient for visual search.

Visual search requires that images be actually identifiable by Google’s visual recognition systems. A compressed, lazy-loaded WebP image with good alt text that shows a blurred or ambiguous subject will perform well on Core Web Vitals and poorly in Lens. The two requirements are complementary but address different problems.

Measuring visual search performance

Google Search Console’s Performance report separates the two kinds of visual traffic. Under “Search type”, “Image” covers the Images tab in Google Search. Since 24 September 2026, “Web” has a “Multimodal” sub-option covering web searches that used an image: Google Lens, Circle to Search on Android, image uploads to Google Search and Chrome’s right-click “Search this image”.⁶ The same filter is available in the Generative AI features report. There is no query data for multimodal searches, because the input was an image rather than text, so the Pages view is where to look.

Outside Search Console, traffic from Lens and Circle to Search arrives in analytics as ordinary Google organic traffic and is not distinguished from standard search sessions.

Footnotes

1. Google image SEO best practices — Google Search Central ↩ ↩²

2. Celebrating 25 years of visual search innovation — Google, 14 July 2026. ↩

3. AI Mode in Google Search adds multimodal search — Google Blog ↩

4. Google updates: AI-Organised Search, Google Lens, and more — Google Blog ↩

5. Image metadata in Google Images — Google Search Central ↩

6. Announcing web multimodal Search performance reporting in Search Console — Google Search Central, 24 September 2026. ↩