To the best of our knowledge neither of the two previously published methods for price prediction is publicly available, so we employed custom versions instead (see Methods). We first compared CoPriNet against a retrosynthesisbased implementation. While this approach is more accurate than CoPriNet by almost 0.1 in PCC (see Supplementary Information Table ), it is also 3 orders of magnitude slower (~1-10 vs ~1000 compounds/s on a single GPU). Indeed, CoPriNet throughput is comparable to fast SA estimations such as RAscore or SYBA, and thus eminently suitable for high-throughput studies, overcoming one of the main limitations of retrosynthesisbased methods. In the case of QS$R, we conducted two experiments. First, we computed CoPriNet scores on the QS$R testing dataset, that was collected before the year 2019 and as such, prices could have changed considerably. Even so, we found a f1-score > 0.8, inferior to the value reported in the QS$R publication, but still high, especially when considering the time difference between their test dataset and the CoPriNet training set. Next, we trained the QS$R Multilayer Perceptron (MLP) model on the CoPriNet dataset. For this experiment, we observed that the QS$R model learning curves saturate sooner, achieving worse performance than CoPriNet in both training and validation sets and showing signs of overfitting (see Supplementary Information Figure ). The MLP model outperforms all the studied SA scores, achieving a PCC of 0.58. This PCC value, lower than the 0.77 measured for CoPriNet, suggest that although the simple MLP used in QS$R is able to capture some compound-price relationships, it is not able to exploit the massive amounts of data that we employed for CoPriNet training, and thus, its usage should be limited to QSARlike situations.