The GDI-GNN model shows strong performance on EXT-Zamora dataset; however, its prediction accuracy is likely overestimated due to 16 of the 38 test set molecules being included in the training. In contrast, its performance on the EXT-SAMPL9 set, which has no overlap with the training data, is lower. The GH-GNN model generally shows lower performance, and since its training data is not publicly available, we could not identify potential overlaps of training and test data. Interestingly, activity coefficient GNN models are performing at level comparable to the top five models from the SAMPL9 challenge ). Yet, the activity coefficient GNN models show lower accuracy than the D-MPNNs directly trained on partition coefficients.