Another important objective in this study is to understand how to efficiently apply a BO-guided workflow to optimize multiple experimental targets. Specifically, we consider solutions where the data collection is per-formed in batches to be more compatible with experimental workflows. With traditional BO, we would collect data sequentially and build surrogate models for each experimental target independently. This is not convenient for experimental data gathering, especially since each sample characterisation produces information on multiple targets. We also consider a strategy for shared data collection, where the experiments recommended to advance different targets are combined into one batch and performed at the same time. This conveniently drives the data collection towards different areas of the design space, and we deploy two acquisition strategies in parallel to further boost exploration. Batch characterization outcomes are then used to advance the models over all experimental targets.