• EDBO 4 : This package also implements BO based on GPs, where it considers three descriptors to encode the categorical variables, which are density functional theory (DFT) , Mordred , and one-hot encoding. Here, continuous descriptors such as DFT and Mordred are used to encode categorical variables so that GP with kernels that are designed for continuous design spaces can also be used/tested. Different from BoTorch, EDBO pre-trains the GP model with data from the literature for the following two reactions: Suzuki-Miyaura reaction , consisting of 3,696 reactions in the dataset, and the Buchwald-Hartwig reaction, whose training dataset consists of 3,960 unique reactions . While EDBO, similar to other BO methods such as BoTorch, improves with additional training samples and can suggest optimal solutions even with poorly trained surrogate models , its key advantage lies in its pre-training phase. This pre-training makes EDBO highly efficient, particularly for optimizing re-action conditions when the target reactions share similar features with the training datasets Since the target problems often only involve categorical variables with finite options, the workflow of EDBO involves pre-generating all the possible combinations. The solving process involves an initialsampling and an active-learning stage. Samples are randomly selected from the pre-generated combinations during initial sampling. While in the active learning stage, rather than searching within defined bounds, it exhaustively enumerates the entire domain, selecting the point with the lowest acquisition function evaluation. For mixed-integer cases, continuous variables first need to be discretized. The enumeration procedure in the acquisition step can become computationally expensive as the number of discretization steps increases, highlighting the trade-off between computational time and achieving a better representation of the original domain. Regarding constraint handling, there is currently no implementation integrated into EDBO.