As mentioned above, current generative AI methods generally suffer from the lack of prediction accuracy. Thus, learning from past drug hunting experiences, we deliberately bias the AI method to generate compounds that are similar to active molecules already discovered. We approach this problem by relying on the similarity principle, which states that similar molecules tend to have similar properties. Some advantages to this approach are discussed below. First, by generating molecules similar to the initial set available in the project, confidence in the predictions can be high because they remain in the applicability domain of the model. This is contrary to expressive methods that are designed to fully explore chemical space and generate structurally diverse compounds, which are consequently also the most uncertain to predict. Second, for similar compounds, the same chemical intermediates and established synthetic routes can often be re-used, facilitating speedy progress. Third, sometimes certain structural fragments (e.g., "privileged structures" 77 ) are difficult to replace without severe drops in potency due to specific ligand-protein interactions.