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mixed results, many benchmarks resistant to any shape based query (eralpha, hsp90)  optimizing method of rdkit was able to generate better than random AUCs compared to pre-alignment methods (erbeta, factor xia, fak, and hivrt)  FOMS alignment methods dramatically outperformed other methods in two cases (pka, rhok) due to correct position of fragment with key interactions. FOMS provides a rapid means of template docking \cite{Ruiz_Carmona_2014, abagyan2015icm}  with shape-based scoring. Choice of fragment critical Shape constraints generally tracked or improved upon the performance of FOMS similarity ranking. For cathg, shape constraints provided the most meaningful virtual screening result as they could select for a subshape of the query ligand while the other methods must rank the full shape.