The primary challenge when using multi-task Bayesian optimization is its tendency to bias towards the best conditions found in a single auxiliary task, as shown in our in silico studies. However, our results demonstrate that additional useful auxiliary tasks can reduce the impact of a noisy, lowyielding auxiliary task. Interestingly, we also noticed that the benefit of MTBO use over STBO increases as the number of categorical options increases. Indeed, our experimental case studies each had 20 categorical options, and we found MTBO to perform very well as we proceeded to add multiple auxiliary tasks. Future work could use a more exploratory acquisition function in combination with the multi-task model to strike the right balance between biasing towards the auxiliary task data and exploring untested conditions.