We chose the prediction of reaction yield for an unseen substrate (Fig. ) as a task of practical interest. All reactions that feature a given substrate were held-out; after training on the rest of the dataset, the model predicts the yields of the held-out reactions. Those results were aggregated over all substrates in the dataset. While the DFT model showed encouraging results on the substrate split task, the question of whether the reported predictive performance (R 2 = 0.33) is of practical interest is not clear. Therefore, we designed a realistic classification task, where the model classifies reactions using an unseen substrate in two classes, high yields (> 50) and low yields (≤ 50). This use case corresponds to the situation where a chemist wants to explore reactions with a new substrate, and relies on the model's prediction to discard low yield substrates, and to prioritize efficient ones. On this task, the DFT method reached high predictive performance (with a ROC-AUC, a performance metric for classifiers, of 0.74, see Fig. ). This highlights a practical application of yield prediction models that can be achieved with existing methods.