detail in Sec. 7. Third, we need some kind of optimization component, which either returns the best molecules scored so far, or pushes the construction algorithm towards higher scoring molecules. Fig. shows the how the common steps fit together. Virtual Screening (VS) is named due to its similarity to high-throughput screening. Generally, in VS, first a virtual library, i.e. a dataset, of molecules is generated, and stored. Then, in a second step, all molecules in the library are scored using the scoring function, and eventually the highest scoring molecules are selected. De Novo Design (DND) usually combines three components: molecule generation of novel molecules from scratch, the scoring function, and an optimization, search, or reinforcement learning routine that drives the algorithm to generate higher scoring molecules. The latter drives the molecule generator to create higher-scoring molecules. Hierarchical Virtual Screening, where an informer library is screened and then locally expanded, and Combinatorial Fragment Space Search can be seen as instantiations of DND. Generative Models are machine learning models that can construct molecules, and have been used as part of DND and VS. Virtual Screening and de novo Design are thus means to the same end: an exploration of chemical space to seek out high-scoring molecules. In practice, VS/DND are often used to generate focused libraries using computationally cheaper scoring functions, which are then narrowed down in a "funnel"-like setup with increasingly expensive scorers.