The aforementioned findings suggest a diminishing suitability of chat oriented LLMs for classification tasks over time, a function beyond their intended design. This observation specifically applies to LLMs tailored for conversational or human interaction purposes, rather than specialized LLMs trained on domain-specific data. Unfortunately, the latter do not provide the ease of access and usability that GPT models do. Consequently, we expect that LLMs will increasingly be employed in human interaction settings, facilitating the integration of various chemical tools through natural language interfaces as is being pioneered by Bran and Boiko et al. Finally, we could demonstrate in the present study that classical machine learning techniques, such as SVMs trained on MAP4C fingerprint encodings, can achieve state-of-the-art performance in the prediction of antimicrobial activity and hemolysis. This finding is especially interesting, as it showcases that good performance can be achieved by less complex models, putting the emphasis on data quality rather than model complexity.