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Shape constraint search generally tracked or improved upon the performance of FOMS similarity ranking (e.g. Figure~\ref{cathg}). As shown in Table~\ref{pvaltable}, shape constraints were able to generate statistically significant ($p < 0.01$) enriched subsets for six of the ten targets.  Unlike whole-molecule shape similarity, shape constraints can select for a \textit{subshape} of the query ligand (i.e., perform partial shape matching \cite{Tangelder_2007}) and specifically filter out potential clashes with the receptor. Furthermore, shape constraints support an indexing search algorithm that scales sub-linearly. In our evaluation, shape constraint searches were orders of magnitude faster than even the FOMS similarity search and generally took well under a second.   Shape constraints provide a novel and unique method for specifying molecular shape queries. Although we have investigated automatically generated shape constraints, our assumption is that intelligently designed constraints created by human experts will substantially outperform our interaction point based constraints. The speed of shape constraint searches enables interactive analysis and refinement of shape queries where the expert user sculpts their desired molecule guided by the results of searches of full-sized databases. We anticipate that fragment-oriented shape constraint constraints  will prove a useful tool in both fragment-based drug discovery workflows, which are naturally centered around a fragment, and lead optimization workflows, where the core scaffold of the lead compound can serve as a fragment.