Figure shows a few example molecules randomly picked from the final iteration for each optimization task. It is evident that, by optimizing a single objective function, the model exploits its ability to freely navigate the chemical space and solely focuses on this very objective. While the optimization of drug-likeness obviously results in pleasant-looking molecules, the three other optimization tasks result into comparably "unusual" chemistry. Since long aliphatic chains are both maximizing the partition coefficient while being scored as easy to synthesize, they are the inevitable outcome when optimizing for penalized logP. Moreover, if the objective is solely maximizing the prediction of a QSAR model, the resulting molecules will aggregate the evidence the QSAR model found in the potent molecules of its training set. This, however, will most likely guide the optimizer into parts of the chemical space that are not well covered by training set molecules, leading to inaccurate and overoptimistic predictions. Thus, final molecules are likely to be artifacts of the underlying QSAR model. Summing up, we demonstrated that our method is able to very quickly improve upon a given starting molecule in terms of predicted drug-likeness or predicted biological activity. This confirms that our optimization method is able to navigate the chemical space defined by our pretrained embedding and perform single-parameter optimization in a timely fashion. However, these examples are far from actual drug design cases. At this stage, we do not apply any structural constraints for guiding the structure generation. This means that the new, optimized compounds might be structurally remote from the defined starting points or contain toxic or unstable moieties (e.g. 1,3-Pentadiyne substructure in Figure ). Usually, drug discovery projects are confined within chemical series and closely related analogues. Hence, we propose to add constraints on the chemical structure during optimization in the following section.