In Table we compare the downstream molecular optimisation performance of a number of pre-trained Chemformer models with existing implementations. In particular, we examine the performance of all three pre-training tasks with base Chemformer models, after fine-tuning for 100 epochs, along with a Chemformer-Large model (pre-trained on the combined task) fine-tuned for 80 epochs. For the Transformer and Transformer-R benchmarks we use the published models, but examine only top-1 performance. From the table we can see that, while all Chemformer models perform strongly in comparison to existing benchmarks, the smaller Chemformer models outperform the larger on the percentage of desirable molecules generated. The Transformer-R model, however, generates more molecules which meet the MMP-33 requirement. This metric measures the percentage of generated molecules for which, firstly, a single transformation has been applied to the starting molecule, and, secondly, the ratio between the number of heavy atoms (non-hydrogen atoms) in the transformation and the number of heavy atoms in the entire molecule is not greater than 0.33. All models we examined generated a very high proportion of valid molecules, but the Chemformer models generated slightly more than existing approaches.