Previous prospective applications of CLMs for de novo molecule generation relied on "temperature sampling" of virtual molecule libraries. Temperature sampling generates molecules by weighted random sampling from the probability distributions learnt by the CLM during training. Such generated SMILES strings might not always be chemically meaningful (invalid strings) or they do not match the feature distribution of the training data. Additional methods are usually needed to select the most promising designs from the virtual molecule libraries, e.g., based on the similarity to known bioactive molecules or activity predictions . Here, we used the beam search algorithm as a model-intrinsic alternative to temperature sampling . This method enables the automatic generation and prioritization of the designs, without strictly requiring additional compound scoring. Beam search scoring was successfully validated in a prospective application aiming to generate new retinoic acid related orphan receptor (ROR) ligands from scratch.