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\section*{Discussion}  Theuses of the challenging  MUV dataset dataset, with its focus on eliminating analogue bias, is particularly resistant to single-query shape-based virtual screens \cite{Tiikkainen_2009}. This is reflected in our overall results, shown in Figure~\ref{aucs}, where only two targets (Rho and PKA) achieve AUCs where the 95\% confidence interval does not overlap with 0.5 (random performance).  produces generally. 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