A new algorithm for identifying three-dimensional configurations of chemical features common to a set of molecules is described. The algorithm scores each configuration based both on the degree to which it is common to the input set and its estimated rarity. The algorithm can be applied to molecules with large (several hundred) conformational models. Results from the application of this algorithm to three data sets are discussed: PAF antagonists, HIV reverse transcriptase inhibitors, and HIV protease inhibitors. Of particular interest is a common configuration identified for a set of HIV reverse transcriptase inhibitors; this configuration is shared by two new, potent inhibitors that were recently described in the literature.