After training solution fastprop and chemprop models, their performance on extrapolation was evaluated on the Leeds and SolProp test sets and benchmarked against the Vermiere model as made available via a Python package in the original publication. We observe that the Vermiere model performs poorly on the Leeds dataset, with RMSE = 2.16 and % log S ± 1 = 41.2% (Figure ). On inspection there is observable systematic bias, with the model often severely over-predicting the solubility. In contrast, both the solution fastprop and chemprop models perform similarly well, with RMSE = 0.95 and % log S ± 1 = 73.8% for fastprop and RMSE = 0.99 and % log S ± 1 = 70.9% for chemprop (Figure ). The systematic bias is greatly reduced in both models.