The influx of generative models applied to de novo drug design is both exciting and frustrating. It is exciting in the sense that complex objectives can be optimised and de novo designed molecules are beginning to be prospectively validated . It is also frustrating in the sense that there is often a lack of consideration for the quality of chemistry generated , many models are still applied to prospectively irrelevant objectives (such as rediscovery or penalised logP ), and scientific significance of proposed de novo designs is often overlooked . However, it is understandable that due to the sheer number of approaches not all models can be prospectively validated, that many new methods stem from the computer science and machine learning domain where drug design expertise may be lacking and therefore simple, easy-to-implement objectives are preferred, and finally that benchmarks are still needed to compare approaches. However, these should relate to the real-world as much as possible.