We consider a general, multi-objective scoring function f : M → R which, given molecule m ∈ M, yields a numerical score s ∈ R as a weighted sum of the objectives. We note that more sophisticated ways for multiobjective scoring and optimization exist, however, this goes beyond the scope of this article . Common objectives could be fingerprint similarity towards a reference molecule, docking, machine learning/Quantitative Structure-Activity Relationships (QSAR), or more sophisticated physics-based simulators such as free energy pertubation (FEP), or computable physicochemical or topological properties, which can be as simple molecular weight, ring counts or the presence or absence of substructures. Second, we assume we have a molecule representation and a construction algorithm, which will be discussed in more Accessibility here refers to a combination of ease of access and synthesis, but also other important criteria such as stability and chemical reasonableness. On-shelf compounds, for example from a high throughput screening (HTS) collection, are readily accessible. Virtual make-on-demand compound datasets that rely on enumeration of a small number of robust reactions against building blocks represent larger chemical spaces, but suffer from the need to store the full data sets, which becomes intractable quickly. Synthesis-constrained or biased de novo design can give molecules as accessible as in enumerated VS libraries, but allows to access much larger spaces as generated molecules do not need to be stored. Unrestricted de novo design, which can combine atoms and bonds under valency constraints, provides the largest theoretically possible chemical space. However, most of these molecules would not be synthesizable.