Here we present a simple, highly automated, and data-driven approach for on-the-fly optimization of computational resource allocation for high-throughput TI RBFE and TI ABFE simulations. The goal of this protocol is to utilize the minimal resources necessary to achieve a convergence with a desired accuracy for each individual simulation with as little user input as possible. We begin by describing the theory behind alchemical BFE simulations and the simulation parameters employed for each system studied in this work. Next, we illustrate the workflow and concepts behind our on-the-fly optimization algorithm. We then demonstrate our RBFE implementation on the cyclin-dependent kinase 2 (CDK2) benchmark system using the same set of ligands as Song et al. and compare our accuracy with respect to experimental results against theirs. In order to examine the performance of our protocol on more difficult and flexible systems, we apply several implementations of our protocol to several ligand mutations of the SARS-CoV-2 papain-like protease (PLpro) that we have performed in previous work. As PLpro is less characterized than CDK2, we compare our results against long simulations as opposed to experimental results. Next, we apply our ABFE implementations to compute the affinity of the T4 Lysozyme L99A/M102Q mutant to N-phenylglycinonitrile (PDB ID: 2RBN ) and compare the accuracy with respect to experimental results against that of a protocol we utilized in our previous work. We also compare several optimized protocol implementations on the PLpro ligand N-[(1R)-1-naphthalen-1-ylethyl]benzamide against long simulations. We demonstrate that our protocol maintains accuracy while yielding increases in computational efficiency when compared to previously published sampling schemes. Finally, we conclude by making prescriptions for future high-throughput alchemical BFE drug discovery campaigns.