This study examines the differences between Web image and textual queries, and attempts to develop an analytic model to investigate their implications for Web image retrieval systems. A large number of Web queries from image and textual search engines were analyzed and compared based on their factual characteristics, query types, and search interests. A feasible analytic model employing the concepts of uniqueness and refinement was adapted to categorize query types and analyze the characteristics of failed queries. Useful results include the findings that image requests may have higher specificity and contain more refined queries (especially among failed queries), and that the queries were refined more by interpretive attributes than by reactive and perceptual attributes. Current text retrieval technology is not capable of fulfilling such complex image requests. It is suggested that there is a need to increase the number of appropriate annotations for Web images and to utilize more advanced retrieval techniques for more effective Web image searching. Few previous large-scale studies have investigated visual information retrieval using image search engines. Thus, this study provides results that might enhance our understanding of Web image searching behavior and suggests implications for the improvement of current Web image search engines.