We find that high P(active) building blocks generally are much closer (and therefore more similar) to one another than they are to random building blocks (Figure -F, Table ). Here, we define high P(active) building blocks as the top 10 by descending P(active) value. On average, the Euclidean distance from a high P(active) to a randomly selected building block is twice as large as the distance from one high P(active) building block to another (Figure -F, Table ). This supports our hypothesis that similar building blocks have similar P(active) values and motivates our next step: to predict the P(active) value of an untested building block based on the P(active) values of the building block(s) most similar to it. We also test if 2D or 3D Tanimoto similarity results in clearer separation of clusters. We observe more random separation between building blocks of similar P(active) value when using 2D Tanimoto similarity (see Section 4.3) instead of 3D Tanimoto combo (Figure ). The UMAP projections from 2D Tanimoto show building blocks with similar P(active) value scattered throughout chemical space with less structure (Figure , Table ). A potential explanation for this is because 3D Tanimoto takes into account multi-conformer overlays of 3D structures, it is able to better relate the binding ability of molecules compared to 2D Tanimoto.