Given our inter-pose features, we can now compute the extent to which ligands binding to the same receptor do so in a similar manner. We first define the 'native distribution', which looks at the inter-pose similarity between only poses with <2 Å RMSD from the ground truth, denoted as f (s(•, •)| Native). We next define the 'reference distribution' to be the inter-pose similarity between all pairs of the top 100 poses output by gnina, denoted as f (s(•, •)| Reference). Both distributions are determined via a Gaussian kernel density estimate on each of the inter-pose features. Following Paggi et al. , the Gaussian kernel density estimate uses a standard deviation of 0.03 for interaction similarities and 0.18 for MCSS RMSD. Reflected boundaries are used to reduce bias near the boundaries.