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\subsection*{Virtual Screening Evaluation}  In order to investigate the utility of the FOMS approach we consider both the ability of shape constraint filters to generate enriched subsets and the quality of shape similarity rankings generated using fragment aligned molecules. We compare to VAMS, which aligns all molecules to a canonical reference system based on their moments of inertia, and rdShape, the shape alignment module of rdkit \cite{rdkit} which dynamically aligns shapes to maximize their overlap. overlap using Open3DALIGN \cite{Tosco_2011}.  Results are reported using receiver operating characteristic curves which plot the false positive rate (FPR) with respect to the true positive rate (TPR) as the classification sensitivity threshold is changed for a ranking. The area under the curve (AUC) is reported.  For each target, conformers of the active and decoy compounds were generated using rdkit\cite{rdkit}. A maximum of 100 conformers with a minimum RMSD difference of 0.7{\AA} and an energy window cutoff of 10 were generated for each compound. For each fragment considered, the corresponding conformers were extracted intoeach  fragment-specific subset. subsets.  The subsets were then preprocessed to create VAMS and FOMS search databases. The VAMS database stores a single pose for each conformation aligned along its moments of inertia. For FOMS, if a compound contains multiple instances of the anchor fragment or the fragment contains symmetries, multiple poses per a conformation are stored to account for the multiple fragments/symmetries. For each target, a single reference structure was identified by searching BindingDB,\cite{Liu_2007} PDBbind,\cite{Wang_2005} and Binding MOAD\cite{Hu_2005} for the complex with the best binding affinity.  \textbf{choice of reference ligand/receptor}\\  \textbf{shape constraint generation}