Examining the molecular property prediction tasks in more detail, we continue to see that transfer learning provides a performance boost. Table outlines the results of Chemformer models on these tasks. The most significant increase in performance from transfer learning is witnessed on the lipophilicity task; the performance boost on the ESOL and free solvation datasets is more modest. The table also compares the Chemformer models against an SVR baseline, trained as described in Section 2. The SVR is able to beat the randomly initialised Chemformer model on the lipophilicity task, but is otherwise outperformed by all other models across all tasks. The combined model outperforms all others, including the SVR, on all three tasks. Figure provides a more detailed view of the results on the biological activity prediction tasks. The performance of each Chemformer model is compared to that of the SVR for each of the 133 tasks. While there is a lot of variation in the results for each gene -some tasks are challenging irrespective of the model -the improvement provided by transfer learning is clear. All three pre-trained models perform significantly better than the random initialised model, and, again, the model pre-trained on the combined task is the strongest performer. However, despite the improvement provided by pre-training, none of the Chemformer models are able to beat the SVR baseline on average across all tasks. The full set of results on the 133 tasks can be found in the supplementary information.