Generalized subset designs and model-driven approaches such as model-based design of experiments (MBDoEs) have emerged as an answer to the shortcomings of the traditional factorial design of experiments. Generalized subset design involves efficiently selecting representative subsets from a large pool of experimental points by employing combinatorial structures like orthogonal arrays and Latin squares. These subsets are transformed into symmetrical/hypercube representations, where optimal design planes are generated and later reverted to the original experimental space . Generalized subset designs are robust but cannot be adaptively updated with information gained and, therefore, can be inefficient when the design space is large. On the other hand, MBDoE aims to fit a reasonably well-performing semiempirical or empirical model with a small number of informationrich samples . The parameters of the model can be estimated by different optimality criteria. For example, the commonly used D-optimal design 15 can be used to select samples to estimate the parameters by maximizing the determinant of the information matrix. One limitation of MBDoE is its restricted exploration capability 4 , and the need for the system to be well understood for an accurate model to be derived. Additionally, MBDoE primarily relies on predefined experimental designs, which may inadvertently overlook certain regions of the experimental space. This limitation can impede the discovery of patterns or relationships within the data. For example, MBDoE may fail to consider the impact of unconventional temperatures or unique solvents if these factors are not incorporated into the initial model assumptions, thereby potentially missing out on superior reaction conditions that could significantly improve the yield of a target product.