In general, models for compounds represented by their fingerprints outperformed models for SMILES representation in clustering-based and random strategies, for both AUC and BEDROC (20), see Figure . This can be due to the specific architectures employed in each case: the compound analysis block for SMILES-encoding is based on CNN and LSTM to capture the sequence structure, while the compound analysis block for fingerprints is based on a single feed-forward neural network. This di↵erence in model complexity and by extension, in the number of parameters, could have resulted in a poorly fitted SMILES-encoded model.