Results from four sets of models have been reported: ECFP-based models, NeuralFP-based graph models, NeuralFP, feature-based models built on other types of chemical features, as well as one of the state-of-the-art graph models, O-GNN. We found that the state-of-the-art graph machine learning models outperformed or were at least on par with the feature-based methods in all tasks. To support these results, we conducted a data-efficiency analysis to provide guidance on when graph models are advantaged over feature-based approaches and examine the correlation of residual errors across both methods. We found the graph machine learning architecture an exemplary tool for molecular property prediction tasks on datasets exceeding 1000 observations and competitive with conventional feature-based models down to several hundred observations. The state-of-the-art graph machine learning methods provide a rapid and accurate approach for environmental chemistry property prediction.