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We evaluated both shape constraints and shape similarity using the single reference complex. For the shape constraint search we evaluated multiple maximum shape constraints by shrinking the receptor shape by 0, 0.5, 1.0, 1.5, and 2.0 {\AA}ngstroms. For the minimum shape constraints we used interaction points with minimal radius (one voxel). We evaluated every possible query of interaction points. That is, if $n$ interaction points were identified, we evaluated $2^n$ minimum shapes. Selecting subsets of interaction points reduces the dependency of the query on the full shape of the reference molecule and more closely mimics the use case where the shape constraints are manually constructed by an expert to select only the most important interactions. As each shape constraint query is a filter, it does not return a total ranking of database compounds, so we report the true positive and false positive rate of the selected subset. We calculate a p-value for the returned subset (relative to the null hypothesis of random selection) using a hypergeometric test. Reported p-values are corrected for multiple comparisons using the Bonferroni correction (the calculated p-value is multiplied by the number of comparisons).  Shape similarity results are reported using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. This metric is not biased by the number of compounds and has well-developed statistical properties \cite{Jain_2007}. The AUC is equivalent to the probability that a randomly selected active and randomly selected inactive compound will be correctly ranked by a method. An AUC of 0.5 corresponds to random chance while a perfect predictor exhibits an AUC of  1.0.