Google's new "visual search fan-out" feature enhances its ability to analyze images by identifying not just main subjects but also subtle details and secondary objects. This means that instead of simply matching searches for a "blue backpack," Google can generate a wide range of relevant queries based on the entire context of the image. For SEO, this change is significant because it shifts how images need to be optimized. If crucial visual details aren't reflected in accompanying text, websites risk becoming invisible to potential search traffic. The author has developed a tool for Screaming Frog that leverages OpenAI’s Vision API to analyze images and generate comprehensive search queries. It focuses on important product images while ignoring logos and UI elements, performing a thorough visual analysis. For instance, an analysis of a coffee maker image revealed detailed attributes like materials and features that traditional SEO methods might overlook. It generated specific queries that real users might search for, highlighting the gap between standard practices and the potential offered by visual search. After evaluating numerous e-commerce sites, the author found common issues such as vague color descriptions and missing context cues. Many sites fail to optimize for implied use cases suggested by images, like a work-from-home setup indicated by a laptop and coffee mug. The tool allows users to identify these gaps and improve their image descriptions, alt text, and overall content strategy. This approach not only enhances visibility in search results but also aligns with Google's evolving understanding of visual information.