Next, substructure-based fingerprints like MACCS, ESTATE, PubChem and KR tend to achieve the highest Jaccard-Tanimoto similarity scores. This is consistent with their reliance on predefined fragments, rather than processing each molecular graph individually. Since the fragments chosen by these fingerprints were defined for small molecules, only a fraction of them is usually found in NPs, while other highly informative NP-like substructures are not encoded. This reduces the average bit variance across the fingerprints, leading to more similar vectors overall. These types of embeddings can therefore be problematic for the NP chemical space, unless custom fragments are added to account for the molecular distribution shift and feature selection is used to remove uninformative bits. This issue seems especially pronounced for MACCS and KR, since they achieve lower median similarity scores on the Drug Repurposing Hub, shifting from 0.40 and 0.21 to 0.32 and 0.13, while PubChem and ESTATE remain comparable. This trend reflects the focus MACCS and KR have on drug discovery, thus biasing the fragment choice on relevant motifs for the drug-like chemical space. Both path-based and circular fingerprints have median values of Jaccard-Tanimoto similarity around 0. Finally, the low median Jaccard-Tanimoto scores obtained by MAP4 on both COCONUT and the Drug Repurposing Hub (less than 0.02) could be related to two factors. First, this fingerprint uses categorical encodings, which means that their similarity is computed via the modified Jaccard-Tanimoto similarity.