In a typical image retrieval system, all visual features of query images are used to determine image similarity. Thus, users are left to decidewhether or not to include images that not only contain desirable features but also irrelevant ones. Fewer examples or a contaminated set of more could compromise the retrieval effectiveness of most similarity measures. In this paper, we extend our previous approach that allows users define queries by specifying relevant features present in image examples. The extended technique support queries decomposed in multiple clusters, each forming a subquery. Our experimental results have shown a remarkable improvement in retrieval performance.