Chemists have long sought experimental and computational platforms that can rapidly identify successful reactions. Recent advances in high-throughput experimentation (HTE) in combination with the emergence of machine learning (ML) algorithms within the field of chemistry provides opportunities to achieve this goal . When employing ML in synthetic chemistry , the domain of applicability (i.e., the ability for the model to make accurate predictions) is defined by the experimental search space, which is constrained to a reasonable number of cost-effective experiments. For example, if the aim is to optimize a singular reaction towards a target molecule (e.g., total synthesis or process chemistry), the goal is the identification of optimal reaction conditions often including a catalyst structure to achieve an excellent result . Bayesian optimizers or multivariate linear regression have emerged as ML tools well-suited for these tasks. In contrast, another central goal often encountered in synthetic chemistry is exploiting generality of a reaction or applying the reaction to a wide range of substrates . For this task, the end user would want to have confidence that a particular reaction would work to give a reasonable yield under a prescribed set of conditions. This is a particularly important part of the drug discovery process wherein a medicinal chemist may be required to prepare libraries of compounds from a central core structure in order to gain structure function relationships . However, in most modern synthetic transformations, the scope of the reaction is not necessarily transferable to unseen targets, especially those with added structural complexity . This is an objective where ML tools have not yet seen widespread use. broad value in pharmaceutical synthesis, particularly in late stages of drug development processes due to the lower cost and toxicity associated with the use of Cu (Fig. ).