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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 rdMolAlign, the shape alignment module of RDKit \cite{rdkit} which aligns shapes to maximize their overlap molecules  using Open3DALIGN \cite{Tosco_2011}. Open3DALIGN aligns using shape and property information by performing an atom-based alignment that attempts to match the largest number of atoms having similar partial charges and MMFF94 atom types.  The input conformers for RDKit alignment were the same aligned poses used with VAMS, and the computed similarity score is the Tanimoto coefficient. 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 a maximum energy difference of 10 kcal/mol were generated for each compound. For each fragment considered, the corresponding conformers were extracted into fragment-specific 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.