When comparing the performance on the forward synthesis and retrosynthesis prediction tasks, we noted that the augmentation approach we employed resulted in stronger top-1 performance, but that the top-5 and top-10 performance was weaker than existing methods. By analysing the output of the beam search we found that the proportion of augmented forms of the same molecule in the beam outputs was significantly larger in the models trained with augmentation than the model trained without augmentation. Figure shows how the augmentation probability affects the top-1, top-5 and top-10 molecular accuracy for the USPTO-50K retrosynthesis prediction task. Fine-tuning with no augmentation provides the lowest top-1 performance but the highest top-5 and top-10 performance. Fine-tuning with p aug ∈ {0.25, 0.5, 0.75, 1.0} all lead to comparable top-1 performance, but top-5 and top-10 performance steadily decrease as p aug is increased. Figure provides an explanation for this effect by examining the percentage of the ten beam outputs that contain an augmented SMILES form of the same reactants. Filling the beam outputs with augmented forms results in a lower diversity when SMILES are converted back to molecules; this causes the top-5 and top-10 results to converge towards the top-1. Our augmentation strategy therefore creates a trade-off between an improvement in top-1 performance with a decrease in top-5 and top-10.