Recursive feature elimination (RFE) is a quick method to gauge an appropriate minimum number of features to achieve reasonable accuracy. Using various models, the random forest retained the fewest features with comparable accuracy, while regression and support vector methods lost accuracy faster, perhaps due to the already streamlined set of features as the inputs (Fig ). However, we also observed a smaller discrepancy between the five cross-validation sets, indicating that feature reduction reduced overfitting in the model. With these various approaches to fixed representations alongside the neural network model, we benchmarked the performance of each hyperparameter-optimized model, as done with ChemProp. Benchmarking was conducted with the 5-fold cross validation scores over the entire chemical space. We compare various summary statistics of various machine learning models, feature-reduced models, and ChemProp's MPNN. The multipleconformer models all outperformed the MPNN models, and also displayed more consistency between metrics.