Herein, we used perplexity to assess the "goodness" of the designs generated by CLMs via multinomial sampling aiming to (i) preserve the advantage of intrinsic molecule ranking, as it can be achieved via beam search 29 , (ii) benefit from the chemical space exploration provided by multinomial sampling , and (iii) provide model-based insights into the most promising molecules for follow-up analysis . Moreover, the perplexity metric identified undesired effects of transfer learning using CLMs, thereby qualifying as a criterion for detecting undesired model biases.