Many prospective de novo design studies based on CLMs used weighted random sampling ("multinomial sampling", often in the form of temperature sampling) for molecule generation . This method samples the most likely SMILES string characters more frequently than the unlikely characters. This feature enables (i) extensive virtual molecule libraries to be generated and (ii) a certain chemical space to be investigated, owing to "fuzzy" (probabilityweighted) random sampling. However, such a sampling strategy can result in molecules that do not possess the physicochemical and biological properties of the training data. Furthermore, because the number of molecules that can potentially be sampled from CLMs significantly exceeds synthetic capacities, and a natural ranking of the generated SMILES does not exist, an additional procedure is required for molecule prioritization, e.g., that based on similarity assessment or activity prediction . We recently introduced the beam search algorithm as an alternative to multinomial sampling. During beam search, the most likely SMILES strings are generated based on the respective character probabilities, thereby alleviating the strict requirement for additional molecule prioritization . The beam search performs chemical space "exploitation" as the algorithm searches for the most probable SMILES strings in a greedy manner. This method generates only a few candidate molecules at the expense of chemical space exploration and design diversity.