We propose the use of Particle Swarm Optimization (PSO) heuristic to optimize molecules in a continuous latent representation. We show that our model is able to consistently optimize molecules with respect to single objectives such as maximizing the predicted drug-likeliness, partition coefficient logP or target binding affinity as modeled by quantitative structure activity relationship (QSAR) model. Not only does our proposed method exhibit competitive or better performance in finding optimal solutions compared to baseline method, it also achieves significant reduction in computational time. In further experiments we show how the proposed method can be utilized to support medicinal chemists in a lead optimization process by proposing in silico solutions for relevant tasks. Finally, we perform multi-objective optimizations of compounds with respect to relevant properties such as specific target activity profiles and pharmacokineticsrelated properties. We demonstrate that our proposed method is consistently able to optimize the joined objective function and results in compounds that exhibit desirable ranges for all included computed properties. Although we show promising results for optimizing molecular properties in this work, it has to be noted that the optimization cycles are based on predicted values for the properties. This can be particularly problematic for QSAR models that are applied outside their domain of applicability. Hence, we advocate the use of our proposed method only in combination with reasonable constraints to parts of the chemical space that can be modeled by the applied functions with reasonable confidence. An even more suitable approach would be combining the in silico optimization with real world experiments in an active learning manner. By doing so, the QSAR model could be refitted while evolving into yet unexplored regions of the chemical space and hopefully remain reasonably accurate in its predictions. Table : Four example compounds as result of the in silico optimization for experiment 1,2 and 3 respectively, separated by horizontal lines. Each compound is shown with its desirability score (dscore) as well as its calculated and predicted molecular properties: Binding affinity to target Epidermal Growth Factor Receptor (EGFR), binding affinity to target asparyl protease b-site APP cleaving enzyme-1 (BACE1), metabolic stability (metstab), solubility from powder(sol), cell permeability (perm) drug-likeness (QED) and synthetic accessibility (SA)