The face centered cubic (FCC) complex concentrated alloys (CCA) compositional space is expansive, making it almost impossible to discover the most promising alloy compositions using conventional approaches. As part of the High Throughput Materials Discovery for Extreme Conditions (HTMDEC) initiative, the compositional space of a CoCrFeNiVAl CCA system has been explored following a combined computational materials science and multi-objective batch-Bayesian optimization strategy searching for alloys with the most optimum mechanical properties under extreme conditions. For each batch, 16 alloys were selected for synthesis at bulk scale, processing, and characterization, which included microstructural, chemical, and mechanical property characterization. For the synthesis, processing, and characterization, special efforts were put into parallelizing and automating each step to increase and modify experimental throughput and efficiency. Within a timeframe of less than 9 months, a total of 5 iterations (80 alloys) were conducted, which established a clear picture of the chemical and microstructural dependencies of the target mechanical properties of strength and strain hardening coefficient. Furthermore, this systematic investigation led to the discovery of compositions with outstanding properties, such as demonstrating a threefold increase in the strain hardening rate during tensile deformation than the rate for other alloys tested. The microstructural origins of these outstanding cases have been explored utilizing EBSD and TEM techniques, which demonstrated the existence of nanoscale twins.