As far as we know, there has not been a comprehensive and systematic study examining how the most common tokenizers and their vocabulary sizes affect Encoders in the context of predicting reaction yields. We thus investigated the influence of various tokenization methods, with different data set sizes and reaction string representations, on the predictive performance of the BEE model when trained on random and out-of-sample splits with the available HTE reaction data sets. The tested tokenization approaches were atomisation, BPE, WordPiece and SentencePiece, at the maximum size of 500, 1000, 5000 and 20,000 tokens in the dictionary. The investigated molecular representations were standardised SMILES, DeepSMILES, SELFIES, MFP "on" bit indices with counts and IUPAC names (Figure ). Given the large number of studies and results, here we focus the discussion on one dataset that is exemplary of the general trends (the Ahneman et al. dataset). The results for all studies are reported in the supporting information (SI) and we call out any significant differences as appropriate. The influence of the different tokenization methods paired with diverse vocabulary sizes and reaction representations onto the resulting tokens are presented in Tables . Based on these results, we expected the chemically informed atomisation of strings and small vocabulary sizes of the alternative tokenizer dictionaries to afford better generalisability to unseen structures, whereas the larger, and thus, much more specific but presumably also overfitted, vocabularies to perform better with random split approaches. It was also anticipated that too small a vocabulary could negatively impact the ability of the model to understand the synthetically relevant connections between tokens, as a small vocabulary results in a high number of tokens per molecule. Additionally, a small dictionary of tokens normally requires longer context window than larger vocabularies due to the increased number of tokens in the input. On the other hand, a small vocabulary needs fewer neurons in the embedding layer, which can result in a smaller total number of parameters to train. The optimal tokenization and representation strategy would strike a balance between good model generalisability, ability to understand synthetic trends based on structures under a low data regime and have good chemistry comprehension, presumably only aggregating into single tokens the most common, repeating functional and structural elements of molecules or reaction elements, such as protective groups or solvents. With the exception for MFP encodings, atomisation results in considerably larger number of tokens per input than other tokenizers, even with the smallest tested vocabulary (Figure ). For example, in case of SMILES, the atomisation returns on average three times higher number of tokens per compound than BPE, WordPiece or SentencePiece with the vocabulary size of 500. We first evaluated the tokenization methods using pre-trained models with the cross-validation-based regression task on random splits of the available HTE data sets. The models had been pre-trained using different text-based representations of the Pistachio data set with different tokenizers, before they were fine-tuned on the yield prediction task with the specific HTE sets (Figure ). As expected, the models with small tokenizer vocabulary sizes (atomisation, and dictionaries of 500 tokens) typically underperformed in comparison to models utilising larger dictionaries for all text-based notations (Figure for the data by Ahneman et al. and Tables ). Generally, atomisation gave worse performance than the conventional tokenizers, and the difference in performance typically increased with the decrease in the fraction of the training data, e.g. an average difference in R 2 values between atomisation and BPE (dictionary of 500 tokens) for SMILES-based representations was 0.015 for 100% of the training data by Ahneman et al., and it increased to 0.034 for 50%, 0.062 for 25% and 0.066 for 10% of the data (Figure , Table ). Overall, BPE and SentencePiece seemed to give the best results with all molecular representations (Table and). In case of SELFIES and MFP encodings, a use of BERT's native WordPiece resulted in significantly worse performance than other tokenization methods. The investigation of potential reasons for this behaviour revealed that WordPiece assigns an unknown token [UNK] to a large portion of compound strings when used in conjunction with SELFIES and MFPbased representations. When WordPiece is unable to find a correct subword in the available vocabulary, the whole word, and in this case a molecule, is tokenized as unknown. The [UNK] token constituted 19-25% of all generated tokens for SELFIES and 12-18% for MFP-based encodings, when tested with the unique reaction compounds from the Pistachio validation set (Figure ). The ratio of the [UNK] tokens increased with the size of the dictionary. The [UNK] tokens were also found for SMILES, DeepSMILES and IUPAC Names, but to a considerably lesser extent. We therefore strongly recommend against using a combination of WordPiece with SELFIES or MFP-based representations.