Palazzesi and Pozzan recently reported a list of over 100 deep generative methods published in the literature between 2017-2020. The methods are innovative and perform well in benchmark studies that measure models' ability to, for example, reproduce property distributions, and generate valid, diverse, and novel molecules. One may thus conclude that generative modelling is essentially a solved problem -given a reward function, we now have the methods to generate molecules that satisfy it. Despite this success, biology and drug discovery remain immensely complex, and it is our viewpoint that current generative methods best serve to augment drug design. To take the next step (full autonomy), calculated predictions need ultrahigh accuracy and for that we need to develop a broader understanding of human biology. The state of AI in drug design may be seen as analogous to the automotive industry. While the future of autonomous vehicles is promising and exciting, we are not near fully autonomous cars yet. Candidate drugs, as well as cars, still require human attention, given the complexity involved and the vast amount of edge cases that are non-trivial to code up efficiently. Thus, humans (with domain knowledge) are still very much needed in the process: to steer the tools and triage the output. In this context, we would like to highlight the Gruenif.ai tool where the user can provide feedback interactively while molecules are generated. Such "human-in-the-loop" methods can be very effective. Future versions of DeepFMPO will include such functionalities.