The Chemformer model was applied to three molecular property datasets from MoleculeNet : ESOL, FreeSolvation and Lipophilicity, containing 1128, 642 and 4200 molecules, respectively. Since we are interested in optimising the model for all three tasks simultaneously, we ensure that all molecules which appear in more than one dataset appear only in the train set. After splitting the remaining molecules we end up with train, validation and test splits corresponding to 75%, 10% and 15% of the dataset, respectively. We generated 20 different random splits in these proportions. The data was pre-processed by scaling the values in the training set to be between 0 and 1. Each of the three datasets was scaled independently and the same scaling functions are used for validation and testing. Due to their size, the ESOL and FreeSolvation datasets were upsampled by factors of 2 and 3, respectively, during training.