Retrieval in image information bases has been traditionally addressed by two different and unreconciled approaches: the first one uses normal query methods on metadata or on a textual description of each item. The second one works on low-level multimedia features (such as color, texture, etc.) and tries to find items that are similar to a specific selected item. Neither of these approaches supports the most common end-user task: the exploration of an information base in order to find the “right” items. This paper describes a prototype system based on dynamic taxonomies, a model for the intelligent exploration of heterogeneous information bases, and shows how the system implements a new access paradigm supporting guided exploration, discovery, and the seamless integration of access through metadata with methods based on low-level multimedia features. Example interactions are discussed, as well as the major implications of this approach.