In this study, we have performed a search for molecular structures that simultaneously satisfy several complex quantum chemical phenomena important in advanced energy applications. We have demonstrated that combining high-fidelity quantum chemistry simulations, machine learning predictive models, and state-of-the-art reinforcement learning strategies is an effective tool in efficiently exploring molecular space. Without being explicitly programmed on how to construct resonantly stabilized radicals with appropriate orbital energies, the RL algorithm learns a range of strategies that lead to high-performance candidates. As the optimization criteria in molecular design challenges grow more complex, the need for efficient search algorithms also grows. The construction and optimization of a surrogate model for an otherwise costly simulation or experiment is widely used in many fields, including molecular design. While in this study, candidates were found with reasonable efficiency (50% of optimized radicals), iterative refinement of the surrogate model with respect to the ground-truth calculations would improve the model's accuracy. Additionally, while molecules with putative synthetic routes were found from among the top-performing candidates, more accurate and faster methods of searching synthetically accessible space are required. Additional refinement of the top-performing candidates is also required before they are likely to be applicable in aqueous organic redox flow batteries. Optimizing solubility with predictive models and including charged moieties in both the training data and action space will be particularly important in achieving high charge density. Addressing these limitations to achieve holistic prediction of improved bipolar redox actives candidates remains a future goal. All software, data, and models generated in this study have been made available as open-source resources (see Methods).