Automated reaction prediction methods have recently made great advances in terms of cost, accuracy, and throughput that enable them to often find low-barrier reaction mechanisms with minimal or no mechanistic guidance. However, many of these algorithms scale extremely poorly with reactant size, limiting their application to very small systems. Even algorithms that can be applied to reactants the size of glucose still need to be combined with a network exploration policy that constrains the number of potential intermediates as the network grows. For example, a naïve exploration, in which all products are retained as potential reactants, grows exponentially with respect to network depth, even without considering bimolecular reactions or catalyzed reactions.