The continued explosion in the growth of image and video databases makes automatic image search and retrieval an extremely important problem. Among the various approaches to Content-based Image Retrieval (CBIR), image similarity based on local point descriptors has shown promising performance. However, this approach suffers from the scalability problem. Although bag-of-words model resolves the scalability problem, it suffers from loss in retrieval accuracy. We circumvent this performance loss by an ensemble ranking approach in which rankings from multiple bag-of-words models are combined to obtain more accurate retrieval results. An unsupervised algorithm is developed to learn the weights for fusing the rankings from multiple bag-of-words models. Experimental results on a database of 100,000 images show that this approach is both efficient and effective in finding visually similar images.