On the molecular property prediction tasks the results show that pre-trained Chemformer models are able to outperform the SVR baseline. On biological activity prediction, however, even the best performing Chemformer model, the combined task model, shows marginally lower performance than the SVR. The SVR models are, however, trained on each activity prediction task separately; meaning 133 models need to be maintained and productionised, in comparison with a single Chemformer encoder. The performance of the SVR models may benefit from this separation, but more work is needed to determine the extent of this performance improvement. For both sets of discriminative tasks, more experimentation is also required to compare our models with existing baselines, including the use of additional molecular fingerprinting algorithms.