Abstract. Content based image retrieval (CBIR) has been well studied in the computer vision and multimedia community. Content free image retrieval (CFIR) methods, and their complementary characteristics to CBIR has not received enough attention in the literature. Performance of CBIR is constrained by the semantic gap between the feature representations and user expectations, while CFIR suffers with sparse logs and cold starts. We fuse both of them in a Bayesian framework to design a hybrid image retrieval system by overcoming their shortcomings. We validate our ideas and report experimental results, both qualitatively and quantitatively. We use our indexing scheme to efficiently represent both features and logs, thereby enabling scalability to millions of images.