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\section*{Discussion}  Using the challenging MUV dataset we achieved highly  mixed results, many benchmarks resistant to any shape based query results. Some targets  (eralpha, hsp90) performed poorly with all the tested shape-based methods. In these cases there likely was a lack of meaningful shape complementary between the benchmark actives and the query ligand.  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