A crucial part of Case-Based Reasoning is retrieving cases that are similar or otherwise relevant to the problem at hand. Traditionally, this has been formulated as a problem of indexing and accessing cases based on sets of predictive features. More generally, however, we can think of retrieval as a problem of recognition. In this light, several limitations of the feature-based approach become apparent. What constitutes a feature? What makes a feature predictive? And how is retrieval possible when the structure of an input is predictive, but its components are not? This paper presents an analysis of some of the limitations of featurebased recognition and describes a process that integrates structural recognition with retrieval. This structural recognition algorithm is designed to augment the retrieval capabilities of case-based reasoners by facilitating the recognition of functional design clich6s, natural laws, and sub problems for which individual features may not be predictive.