We first analyzed the distribution of pairwise similarities across the COCONUT dataset (Figure and Table ) and the Drug Repurposing Hub compounds (Figure ) to understand which fingerprints provide a more granular view for NPs and whether these patterns differ with drug-like molecules. On the COCONUT dataset, Pharmacological fingerprints (PH2 and PH3) have the broadest distribution of pairwise similarities as well as the highest median Jaccard-Tanimoto similarity. Crucially, both distributions consistently reach similarity scores above 0.95, especially for PH2, indicating that even though the dataset is without replicates, according to these embedding many compounds are nearly indistinguishable. This is consistent with how this class of fingerprint is computed: instead of capturing information pertaining to the molecular structure, these embeddings try to describe molecules in terms of how they interact with their biological environment through their pharmacophores. As such, compounds that have very different chemical structures can still have identical pharmacophoric points, which is reflected by their high similarity scores in terms of PH2 and PH3 fingerprints. This shows that these featurization approaches are well suited for scaffold hopping in the NP chemical space, but their inability to separate structurally different compounds might be problematic for other QSAR applications.