Python libraries for general-purpose BO are plentiful. Popular examples include scikit-learn, GPyOpt, HyperOpt, SMAC3, Dragonfly, HEBO, BoTorch, Ax, and Vizier, amongst others. Most of the aforementioned libraries are primarily scoped toward optimization of machine learning (ML) model hyperparameters, and often lack specific functionality requisite for the experimental sciences. For example, the proposed parameters for hyperparameter tuning are generally expected to be executed exactly, while in a SDL it might be impossible to control experimental conditions to high levels of precision. Other requirements for broad applicability of BO in experimental sciences include constrained optimization, with a priori known (physical hardware restrictions, safety concerns) and unknown (failed/abandoned synthesis, inadequate conditions for property measurement) constraint functions, as well as the ability for asynchronous experimental execution (i.e. recommendation of new parameters before a complete batch of corresponding measurements are available). Although several such libraries do contain the low-level infrastructure necessary to implement more advanced optimization techniques, it remains an expert-level task to correctly organize the required building blocks to produce a working prototype. Software libraries for data-driven decision-making in SDLs and experimental sciences have also been reported. While these studies constitute important landmarks in the burgeoning field of SDLs, most target specific experimental frameworks and/or narrow problem types and do not cover the full extent of requirements needed for a truly general-purpose tool.