The search of the Particle Swarm is guided by the objective function that is defined to be maximized. In drug discovery, the optimized objective can be both complex, conflicting, ill-defined or evolving over time. For example, at the early stages of lead discovery, a higher emphasis is put on increasing biological activity and gaining structure-activity-relationship knowledge. A set of targets that should not be hit by the compounds can also be introduced at that stage. Later on, when the overall activity landscape is well understood by the team, the focus of the optimization evolves more towards pharmacokinetics-related properties (ADME) such as improving solubility, metabolic stability or cell permeability, etc. These different objectives can contradict themselves, for example increasing the solubility of a compound might lead to permeability problems. This makes a multi-parameter optimization notoriously challenging.