This present study constitutes a step forward towards automated, self-supervised de novo design. By serving as a model-intrinsic score, perplexity enables the quality assessment of generated molecules. In particular, perplexity might be useful for identifying the most promising molecules, i.e., those that match the probability distribution of the training data as captured by the CLM. This approach enables the comparison of two different methods for SMILES sampling from a trained CLM. The results revealed certain advantages of multinomial sampling over the beam search method for molecule generation. Because perplexity can be used to score SMILES strings based on the information learned by a CLM, the pretraining bias can be identified based on the newly introduced Delta score. Perplexity combined with the Delta score can reveal the most promising molecules, in terms of the fine-tuning objectives, for synthesis and testing. These features can further accelerate drug discovery using CLMs. Future studies will focus on the combination of perplexity with the temperature parameter of multinomial sampling or SMILES augmentation . Furthermore, the combination of CLMs and perplexity scoring bears promise for screening large collections of commercially-available compounds to accelerate model validation . More experiments should be performed to determine the effect of the new approach on molecular de novo designs involving CLMs. Fine-tuning sets. Target selection was limited to molecules satisfying the following conditions (ChEMBL annotation): (i) Organism: Homo sapiens; (ii) protein classification (L1): enzymes, membrane receptors, transcription factors, and single proteins; (iii) number of compounds: 962-2057 molecules (range defined by ChEMBL); (iv) number of activities: at least 2,000 reported pChEMBL values. Ten target proteins were randomly selected from the list of targets. For each of the 10 selected target proteins, sets of 5, 10, 20, and 40 molecules with pChEMBL > 6 were compiled randomly.