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\section*{Discussion}  The MUV dataset, with its emphasis on eliminating analog bias, is particularly challenging for single-query shape similarity methods \cite{Tiikkainen_2009} and this is reflected in uses of  the results. Several challenging MUV dataset produces generally. Some  targets (ER$\alpha$, ER$\beta$, Factor XIa) (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