The impact of various text-based molecular representation methods used during pre-training and finetuning was tested and for each representation type multiple tokenization approaches with different vocabulary sizes were evaluated. We investigated representations based on SMILES, DeepSMILES, Self-Referencing Embedded Strings (SELFIES), Morgan fingerprints (MFPs), and standard English chemical names according to the International Union of Pure and Applied Chemistry (IUPAC) nomenclature. Each of the formats was used to train text tokenizers, and the following tokenization approaches were applied: Byte-Pair Encoding (BPE), WordPiece, Unigram-based SentencePiece, and atomisation approaches which split the input text into smallest chemically meaningful elements appropriate for the given representation. We show that different molecular notations have no significant impact on the effectiveness of the yield prediction. Furthermore, it is determined with statistical significance that for random data splits, the atomisation approaches are generally worse performing than more elaborate tokenization methods with larger vocabulary sizes. For out-of-sample splits in relation to the reaction components, it was found that SentencePiece and BPE perform well when used with SMILES and small or medium vocabulary sizes. Additionally, WordPiece was significantly underperforming in conjunction with SELFIES or MFP-based encodings.