A few years ago, while analyzing real traffic data from editorial and e-commerce projects, I began to notice something unusual. Certain images consistently received visits from Google Images even though they were not “optimized” according to classic SEO playbooks. There was no exact keyword in the alt text, the file name was generic, and still the traffic came. That was when it became clear that discovering long-tail keywords used by Google in images is not an exercise in guessing or creativity, but in investigation. The starting point is understanding that Google Images does not operate as a simple extension of traditional Google Search. It has its own logic, shaped by far more descriptive queries. People searching for text think in concepts. People searching for images think in appearance, context, and visual intent. That distinction changes everything. When someone types a visual query, it usually blends object, physical attributes, environment, and intended use. These combinations rarely appear in classic keyword research tools because those tools were never designed for visual search. In public talks and technical presentations by Google engineers at events such as Search Central Live, one idea comes up repeatedly: an image is interpreted as a collection of signals. The algorithm combines computer vision with verifiable text. There is nothing mystical about it. Everything relies on observable data. A common misconception is that Google “infers” keywords from the image alone. In practice, it validates what it sees with what it reads around the image. The first concrete clues emerged while analyzing Google Search Console reports filtered by image performance. The queries generating impressions are often unexpected. Many would never be used as article titles. They are long, descriptive, almost conversational phrases. They appear because Google connects small fragments of page text, captions, headings, and even distant paragraphs to a specific image. The long tail does not live in a single field. It forms through accumulated context. Another rarely discussed data point is that Google Images learns from collective behavior. When users click, zoom, return, or refine their searches, the system adjusts its understanding of that image. This explains why certain queries begin appearing months after publication. Nothing changed on the site. What changed was the algorithm’s learning, driven by real user behavior. You can observe this directly by comparing newly emerging queries in Search Console without any prior technical changes. Deeper investigation begins when you reverse the process. Instead of trying to “invent” keywords, you study the real queries that already trigger your images, even those with very few impressions. These are valuable signals of how Google is visually describing that content. By expanding on these patterns in new text, captions, and descriptions, you are not speculating. You are responding to data. In interviews given to specialized publications, search quality professionals have consistently emphasized that alt text is not an isolated SEO field, but an accessibility feature that also serves as a semantic anchor. When it confirms what visual algorithms detect, trust increases. When it contradicts that understanding, relevance weakens. This alone dismantles the idea of stuffing keywords into alt attributes. The description has to make sense to a human who cannot see the image. What almost no one explores is the role of seemingly secondary text. Comments, long product descriptions, internal Q&A sections. All of this can be associated with an image if it is semantically aligned. Comparative analyses show that pages with rich, natural descriptions tend to capture more specific long-tail queries in Google Images than minimal pages optimized only for traditional SEO. Discovering long-tail keywords in Google Images, therefore, is not about magic tools. It is about reading signals, interpreting behavior, and respecting how people describe what they want to see. Those who treat images as decoration lose data. Those who treat images as indexable content uncover opportunities few are paying attention to. **Investigative FAQ** How can I know exactly which queries trigger my images today The only reliable source is Google Search Console, filtered by image search performance. Any other tool works with estimates or indirect data. Why do queries appear that I never used in the page text Because Google combines multiple contextual fragments and validates them with visual recognition. The query may be a semantic synthesis, not a literal match. Is it worth creating pages solely to rank in Google Images Data shows that images perform best when embedded in useful, contextual content. Pages built only for image ranking tend to show unstable performance over time. Does Google use generative AI to “invent” image keywords There is no public evidence that Google invents queries. It relies on real search patterns and semantic associations based on observable data. Is there a risk of over-optimizing images for long-tail queries Yes. When text stops sounding natural and starts forcing artificial descriptions, algorithmic trust declines. Data consistently shows correlation with clarity, not excess. If Google learns to describe images by observing how millions of people search for them, how much control do we really have over keywords and how much are we simply learning to listen?