Although active site classification methods can accelerate HTVS on their own, the impact of these ML approaches is much greater when the most active sites can be identified with regression models that predict the strength of O2 binding at each site. There are two diverging approaches to developing regression models on this dataset: either choose a value for the inactive sites, or train only on the data from active sites. We considered both approaches in this work (for the regression model results trained on active and inactive sites, see section 3 of the SI). Using the same set of features as for the classification problem, the multilayer linear perceptron (MLP) regression model trained on the active site data to predict O2 binding energy attained a mean absolute error (MAE) of 0.077 ± 0.010 eV and R 2 of 0.70 (Figure ). The MAE increased to 0.125 eV when the model was restricted to structural features alone. The reduced accuracy in the latter case could perhaps be acceptable for HTVS, indicating that among active sites connectivity may be enough to predict relative activity. However, given that accurate classification of active and inactive sites already requires the electronic features from DFT, including them in the MLP regression model as well comes at no additional cost and clearly does improve the accuracy of the model. We chose to use MLP in the regression scenario because although the random forest regressor (RFR) model we trained achieved nearly identical performance with an MAE of 0.077 ± 0.009 eV, RFR cannot extrapolate outside of the training data. This means that if there were catalysts in a search space that were better than the ones in the training data the RFR model would not be able to predict their higher performance, whereas the MLP model could. Using the full set of features, we conducted the same permutation importance analysis on the model features that we did for the classifier (Figure ). This method once again identifies several electronic features as the most important. In particular, Ionization Energy and Neighbor Charge Difference have an even more pronounced contribution here than in the classification model. As before, this suggests that the carbons that can more readily bind O2 tend to be those that can more easily rehybridize and donate electron density to O2 during bond formation. However, structural features such as Hydrogen Count, Ring Embedding Count, and Ortho or Para still contribute to the model's performance, and their diminished importance likely stems from survivorship bias in the distribution: only sites that actually bind O2 have a quantifiable O2 binding energy, so only these sites can be considered and discrete features that can disable activity entirely will have a smaller apparent impact.