Virtually every real-world materials optimization task involves considering multiple properties of interest, weighing trade-offs of between experiment value and cost, and optimizing many tunable parameters at once. While traditional design of experiments is often used, Bayesian optimization is a dramatically more efficient alternative in many cases. Basic and advanced topics related to Bayesian Optimization will be discussed (as well as specific application examples such as high-dimensional optimization). Applying state-of-the-art algorithms to materials tasks isn’t trivial, even for veteran materials informatics practitioners. Additionally, Python libraries can be cumbersome to learn and use serving as a barrier to entry for interested users. To address these challenges, we also present Honegumi, an interactive script generator for materials-relevant Bayesian optimization using the Ax Platform.