Recent work has demonstrated that the stability of organic radicals, viewed in terms of thermodynamic stabilization and kinetic persistence, can be estimated using density functional theory (DFT). In addition to stability, the electron exchange half-reactions of RFB candidates must have a precise redox potential for optimal performance. For organic radicals, their single-electron half-reaction potentials are determined by their adiabatic electron affinity and ionization energy, which can be reliably estimated via DFT. Computational screening of many requirements for new redox-active moieties is therefore feasible, enabling a highthroughput search for potential candidates. The field of goal-directed molecular optimization has evolved rapidly in recent years, boosted in part by improved machine learning (ML) tools and generative algorithms. Computational lead generation has been predominantly studied in pharmaceutical research, often through generating serialized molecular structures as SMILES strings that resemble a given training database of compounds. While algorithmic approaches like Bayesian optimization can help to efficiently navigate predefined chemical spaces in combination with surrogate models, the number of pre-enumerated chemical species is constrained. In contrast, performing on-the-fly molecular generation during exploration enables searching even larger chemical spaces. Techniques from reinforcement learning (RL) have shown an excellent ability to generate valid molecules with desired properties without relying on an existing database of molecular structures to learn valid structural motifs. In particular, methods based on a direct tree search of molecular structures using techniques such as Monte Carlo Tree Search (MCTS) offer the ability to precisely control the search space of candidate molecules. Most molecular optimization work has been benchmarked on fairly simple and fast-to-compute functions, such as Quantitative Estimate of Druglikeness (QED) or penalized octanol-water partition coefficient (Penalized Log-P) that do not provide a realistic picture of the challenges of molecular design, or optimize just a single objective. Additionally, generative models explore millions of potential candidates during a typical search -precluding the incorporation of more detailed and computationally intensive molecular evaluation criteria using DFT or other high-fidelity simulations. Machine learning (ML) surrogate models, given sufficient training data, have been shown to reproduce quantum chemical calculations at a fraction of the computational cost. In this study, we develop a complex and multi-factored objective function for organic radical charge carriers that includes radical stability, redox potential, and synthesizability considerations backed by O(10 5 ) DFT calculations. We next implement a scalable RL approach based on single-player AlphaZero that guarantees validity and low synthetic accessibility scores for optimized molecules. As an even more ambitious goal than simply improving on the single redox couple performance of either TEMPO or methyl viologen, we sought to find stable radical candidates that simultaneously satisfy both the oxidation and reduction potential requirements for a symmetric aqueous redox flow battery. Compared to the optimization of an asymmetric battery candidate, this requirement imposes a more complex multi-objective optimization challenge, as the quantum chemical energies of two one-electron redox processes must be balanced within a single small radical scaffold. Further, we performed a "synthetically aware" exploration of chemical space by evaluating synthetic accessibility on the fly and pruning the search tree where it enters synthetically or topologically impractical regions. The generative model yielded a large distribution of molecules predicted to meet the desired stability criteria while simultaneously having suitable oxidation and reduction potentials. The accuracy of these ML surrogate predictions was then validated against DFT calculations, with many radical candidates passing all criteria at the DFT level. Furthermore, we performed a post hoc analysis of the predicted retrosynthetic routes for the optimized molecules, finding many molecules with reasonable synthetic pathways. This study demonstrates that goal-directed molecular optimization, coupled with a highly detailed ML surrogate model, can produce realistic candidates for demanding applications. Additionally, this study suggests that stable radical scaffolds for RFBs are likely more abundant than the limited but wellknown set of experimentally characterized motifs.