We focus on the transformer models trained on a set of similar molecular pairs. The molecules are represented as SMILES and the SMILES are tokenized to construct a vocabulary, which contains all possible tokens. After training, the models can generate similar molecules to a given input molecule. In particular, two models trained on varying size of training data are examined in REINVENT: the transformer model trained on around 6.5 million molecular pairs extracted from ChEMBL and the transformer model trained on over 200 billion molecular pairs from PubChem. The molecular pairs with a Tanimoto similarity ≥ 0.5 based on RDKit Morgan fingerprints (radius = 2, with counts) are selected. To generate multiple molecules, the non deterministic, multinomial sampling is used. At each time step, a token is randomly selected based on the probability distribution over the vocabulary.