We elected against the atomisation approach for SMILES due to its relatively worse performance with random splits and many out-of-sample splits than the available alternatives. The clear winner of the evaluations on the random data splits was BPE with medium size vocabulary (Figure and Tables S6, S16-S21). The best performing tokenizers in case of the out-of-sample splits, especially the difficult ones, were SentencePiece and BPE, for which the vocabulary size of 1000 and 500, respectively, seemed to be generally optimal (see Figure and Tables ). BPE tokenizer is significantly faster to train than SentencePiece and requires considerably less memory. Additionally, as the average number of tokens per input is comparable between BPE 500 and SentencePiece 1000 (Figure ), a smaller vocabulary size allows for quicker model training with a smaller memory demand, as it requires fewer neurons in the embedding layer, resulting in smaller overall network size. As BPE with the dictionary size of 500 gave also satisfactory performance with the random splits, we decided to employ BPE with 500 SMILES-based token vocabulary as our tokenizer of choice.