We first demonstrate that E min predicts synthesizability accurately when used by itself on 134,000 molecules in the QM9 database. By comparing predictions between E min and SA, SC, RA, or SYBA Scores, we then find that each score makes correct and incorrect predictions on different molecules. Thus, we find utility in combining E min with other synthesizability scores by using logistic regression models to improve accuracy. Finally, we build a random forest model, trained on standard molecular descriptors, to predict synthesizability. We find E min to consistently improve accuracy when included as an additional feature, with the strongest accuracy improvements found when the training dataset is small. Thus this work demonstrates that E min is not only a strong synthesizability predictor alone, but also an informative descriptor that can be directly combined with existing scores or used alongside other features for machine learning models.