Content-Based Image Retrieval (cbir) is the practical class of techniques used for information retrieval from large image collections. Many CBIR systems allow users to specify their information need by providing an example image. This query-by-example paradigm can be extended to support multiple example images. In this work, we present a large-scale experiment that shows the average performance of querying with multiple examples is significantly better than single-example querying. We also investigate the effects of providing different numbers of example images, the impact of example quality, and the relative performance of functions used to combine image features. Our experiments indicate that three-example queries are more effective than other numbers of examples, and that the minimum combining function is robust for most query types.