This project investigated the performance tradeoff of investing computational resources into acquisition function optimization. We demonstrated the impact of random seed initialization on optimization campaign performance and devised a simple algorithm, Random Retries, to mitigate and improve the consistency and performance of Bayesian optimization on difficult optimization problems.