Apparently the beam search approach coupled with a CLM conserves structural features necessary for the desired bioactivity but still generates innovative and structurally diverse compounds. This result corroborates beam search sampling as a technique for de novo design of bioactive molecules by a CLM. The computational and experimental results suggest two advantages of the beam search algorithm that could further the applicability of AI for de novo design. Firstly, by searching for the most likely molecules a CLM can generate with greedy decoding, the beam search algorithm can serve as an internal measure to probe the suitability of a CLM for the task under analysis. Analysis of the beam search designs can serve to analyze compliance of the CLM designs with the design objectives and hence to evaluate the success of fine-tuning. This is in contrast with temperature sampling, which might lead chemists to consider designs that are not likely according to the model. Beam search may thus be considered an alternative to temperature sampling. It should be noted, however, that the number of designs that can be sampled by beam search is limited compared to temperature sampling, which can virtually generate an infinite number of chemical structures. The two techniques complement each other as alternatives, each with characteristic advantages. The desired application should guide the choice of either strategy. Secondly, beam search sampling could potentially overcome the need for external compound prioritization. If corroborated in future prospective studies, this feature may help to further the applicability of CLMs for de novo molecular design in medicinal chemistry.