Small molecules in a subset of compounds were clustered using the Butina algorithm to represent their structural similarity across the subset. Starting from compounds represented by canonical SMILES, molecular objects were generated using RDKit. Subsequently, RDKit Daylight-like topological fingerprints were generated and the Tanimoto distance matrix was calculated based on these. Finally, the Butina cluster algorithm was applied to the similarity matrix with a varying cutoff for each subset to minimize the number of single-element clusters.