We hypothesised that a graph neural network (GNN) model should be able to detect patterns in molecules that indirectly reflect the drivers of pricing by automatically combining simple features such as the atomic number, aromaticity, or bond type across the different atoms of the molecules. Deep learning models are well suited to identify complex patterns in raw data providing enough data points are used during training, and GNNs are especially effective for molecular graphs, which are moreover fast to compute. We therefore built a model, CoPriNet, that can predict compound prices using as input molecular graphs. CoPriNet was trained as a regression model against catalogue prices and is able to produce far more accurate price predictions on our test set than any of the considered SA measurements (Figure f): the PCC of 0.77 and SRCC of 0.80 are far higher than the best achieved by any of the other methods.