Sometimes a focused virtual screening approach might be desirable. One way of delimiting the chemical space implies carefully selecting the collection to screen. For instance, the database could be confined to drug-like molecules, natural products, synthesizable compounds, or datasets focused on a set of molecular targets, e.g., focused or targeted libraries. Thus, the hits will comply with relevant selection criteria, depending on the project. A second factor to consider is the similarity threshold to define hit compounds. A more flexible hit definition puts novelty and scaffold-hopping in the spotlight, whereas stringent criteria would be suitable for identifying lead compounds, SAR analysis, and hit expansion. We propose that the most extreme hit definition requires that the query and hit molecules are chemical analogs, i.e., molecules with a close synthetical relationship. Since query and hits might as well have arisen from an organic synthesis project, it might be understood as a "pseudo-optimization" algorithm enabling the rapid extraction of purchasable or readily available analogs for experimental SAR exploration. We term this approach ViSAS (Virtual Screening based on Analog Series) since the practical implementation builds upon the analog series formalism by Bajorath et al. ) and subsequently filtering for relevant fullyconnected fragments that include most of the original structure (we require that at least two-thirds of the heavy atoms from the molecule must be included in the fragment's structure). Fragments obtained through this procedure are termed putative cores. Although this method allows every molecule to map to more than a single core, large analog series can be usually summarized in a few cores that comprehensively map all molecules in the series (see Figure ). Nevertheless, keeping a record of all putative cores permits the later inclusion of new molecules, which is the principle on which we base the virtual screening approach proposed here. Algorithms and applications related to the automatic identification of analog series in large data sets have been reviewed. For over a decade, the analog series algorithms derived from matched molecular pair analysis have demonstrated a compelling balance between chemical interpretability and scalability. Recent developments have emphasized the ability of analog series for SAR and activity cliffs rationalization. However, other industrial applications, such as the evaluation of progress in lead optimization,[114] highlight the potential for analog series analyses to assist drug discovery teams dealing with organic synthesis and biological evaluation. The logical formulation of virtual screening from the analog series emerges from the definition of chemical analogs: two molecules are considered analogs if they share a common core structure. Therefore, a typical fragment-and-index approach lists all possible matching cores for molecules in a dataset. Any new molecule that could be reduced to a fragment matching the fragments list would be an analog of the molecule(s) in the dataset indexed to this fragment. It remains only to define a fragmentation procedure and the requirements of a fragment to be considered a valid core. Many different such approaches have been reviewed elsewhere. For instance, exhaustive methods may consider every possible substructure to be a valid core.