A recent focus of the chemical industry is the reduction of its environmental footprint. Proposed routes to this goal include the adoption of green chemistry frameworks that minimize the impact of chemical synthesis and manufacturing at scale and alter process designs to use chemicals with minimal carbon intensity and toxicological risk. Successful application of such a framework requires rapid and accurate assessments of the environmentally relevant properties of prospective chemical components -a task to which machine learning (ML) techniques are particularly well-suited. Machine learning algorithms have proven a useful augmentation to traditional data analytics techniques in the evaluation of environmental impacts of chemical processes. For example, Zhang and Zhang employed a deep-neural-network regression for the prediction of aqueous solubilities of persistent, bioaccumulative, and toxic chemicals. Dawson et al. approximated the intrinsic metabolic clearance rate and plasma bound fraction of toxic chemicals using random forest regression for their application in the toxicokinetic modeling. Zhong et al. trained an ensemble regression model for the prediction of reactivity of organic contaminants toward a variety of oxidants. Other successful applications include the identification of endocrine-disrupting chemicals and direct modeling of environmental impacts from chemical production. These and other use cases demonstrate the broad applicability of machine learning techniques to problems in environmental engineering. Common across the existing literature is the use of chemical features to produce a flattened, vector representation of the complex geometry of an organic molecule. We denote ML models that take this approach as "feature-based" models, as they rely on explicit featur-ization of the molecular structure to construct an input representation. Chemical features have a long history of use in cheminformatics applications and may be broadly classified into two families: molecular descriptors and fingerprints. Molecular descriptors may be understood to abstract molecular structural information into summary statistics, such as molecular weight, polarizability, or numbers of heteroatoms. They have the advantage of being relatively intuitively understood; however, they fail to fully capture the information contained in the molecular structure, and the selection of appropriate molecular descriptors for a given prediction task is often nontrivial. Common examples of descriptor-based features include PaDEL descriptors, Mordred descriptors, and MACCS descriptors. The second class of chemical features, molecular fingerprints, explicitly encode the presence and local environment of functional groups into a feature vector. An example is extendedconnectivity fingerprints (ECFP). The use of molecular fingerprints provides a more direct representation of the molecular structure and simplifies feature selection at the cost of some interpretability relative to molecular features.