The downstream results presented in Section 3 show that the Chemformer model can be successfully applied to both sequence-to-sequence and discriminative tasks. The results also that transfer learning can provide a significant boost to downstream performance and convergence speed. With the exception of the molecular optimisation task, the model pre-trained on the combined task, outperforms all other Chemformer models, and, in some cases, outperforms the existing state-ofthe-art. This result suggests that valuable chemical information is contained in the weights of the pre-trained Chemformer model.