A key challenge in small molecule drug discovery is to find novel chemical compounds with desirable properties. Computational methods have long been used to guide and accelerate the search through the huge chemical space of druglike molecules. In virtual screening, for instance, computational models can be utilized to rank virtual libraries of chemical structures regarding selected properties such as the predicted activity towards a target of interest . However, given the estimated vast amount of druglike molecules (10 23 -10 60 ) , a complete search through this space is computationally infeasible. An alternative approach is to computationally generate new molecules (de novo design) with optimized properties without the need for enumerating large virtual libraries. Heuristic methods such as genetic algorithms were used to optimize selected properties on-the-fly . However, due to the discrete nature of the chemical space, defining rules to transform one molecule into another (e.g. mutation and crossover rules for the genetic algorithms) largely depends on human expert knowledge. Moreover, defining a finite set of possible transformation rules limits the optimization possibilities and thereby promising molecules might be missed. With the recent rise of Deep Learning in the field of computational chemistry, new approaches for de novo drug design have emerged. Segler et al. trained a Recurrent Neural Network (RNN) to model a larger set of molecules represented by the Simplified Molecular Input Line Entry Specification (SMILES) notation . The resulting model was not only able to reproduce the molecules in the training set, but also to generate novel structures. By further training on a focused set of structures with a certain property distribution (e.g. the activity towards a biological target) the novel generated molecules could be enriched with structures following this desired property distribution. Another strategy for fine-tuning a generative model is Reinforcement Learning . Reinforcement Learning aims at learning the optimal set of actions to optimize a defined reward in a given environment. In the case of de novo design, the reward can e.g. be defined by the molecular properties to be optimized. this concept to alter the generative process of a pre-trained RNN, in order to generate more molecules with desirable properties . Besides RNNs trained on the SMILES representation, other groups also utilized Generative Adversarial Neural Networks or other molecular representations such as the molecular graph . While these method differ in how they generate molecules, they all apply Reinforcement Learning to enrich the generated molecules with structures that have desirable properties. The main drawback of such methods is the need to retrain the generative model every time the reward function changes. This becomes impractical in a typical drug discovery project as the optimization criteria usually change over time. A method that decouples the generation of molecules from the optimization problem was originally proposed by Gomez-Bombarelli et al. . In their work, a variational autoencoder was trained on the SMILES notation of a large set of molecules. As a result, a new continuous vector representation of chemical structures was obtained. Points in this continuous space correspond to molecules in the discrete chemical space (as represented by the SMILES notation) and vice versa. Novel structures can be generated by sampling arbitrary points in the continuous space and then transforming them back to the SMILES notation. A molecular transformation can be achieved by a simple shift in the vector representation. Thus, optimizing chemical structures with respect to selected properties can be directly performed by optimizing a reward function in the continuous space. In their work, Gomez-Bombarelli et al. utilized Bayesian Optimization to find points in the space that correspond to molecules with a high drug-likeness and synthetic accessibility. More recently, Jin et al. also used Bayesian Optimization to optimize molecules generated by a variational autoencoder based on molecular graphs . Bayesian Optimization is a powerful method that has proven useful in the optimization of functions that are computationally expensive to evaluate as it needs a comparably low amount of function evaluations . However, its computational complexity increases exponentially with the number of dimensions of the optimization space . In the case of molecular optimization, though, function evaluations are relatively cheap (prediction of molecular properties) and the dimensionality of the search space (continuous molecular representation) relatively high. In this work, we propose the use of a more light weight heuristic optimization method termed Particle Swarm Optimization (PSO). Hartenfeller et al. already proposed in 2008 to apply PSO on a discrete chemical space represented by a large library of molecular building blocks and chemical reactions . Here, we apply PSO in our continuous chemical space reported previously . In three different experiments we show how our proposed method can be utilized to optimize molecules with respect to a single objective, under constraints with regard to chemical substructures and with respect to a multi-objective value function.