Traditional, experimentally-driven trial-and-error searches for new compounds with desired sets of properties have proved to be time consuming and resource intensive. The advent of powerful modeling and simulation techniques as well as readily available time on high-performance computing systems have brought computational and computationally-guided studies to the forefront of the chemical and materials domain. These studies allow us to make increasingly accurate predictions for compounds of interest and uncover promising leads for experimentalist partners to follow up on (see, e.g., Refs. ). An even more recent development has been the emergence of machine learning techniques that empower us to advance, augment, correct, or even replace physics-based modeling and simulation . In the latter scenario, machine learning is used to create data-derived property prediction models that serve as surrogates for physics-based models in order to dramatically accelerate the compound characterization and thus the overall discovery process. In addition to enabling hyperscreening studies, we can employ machine learning to gain a better understanding of the structure-property relationships that determine the behavior of compounds in the corresponding domains of chemical space. The creation of new machine learning prediction models for various target properties and the advancement of the underlying methodology is an active field of research . Key considerations are accuracy, cost, robustness, and range of applicability. Artificial neural networks are a popular and efficient machine learning approach . Multi-layer 'deep' neural networks (DNNs) yield particularly flexible models that have been used to predict an array of chemical properties, including refractive indices , dielectric constants , atomization energies , chemical reactivities , melting points , viscosities , solubilities , and others.