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A key challenge in drug discovery is the efficient search of chemical space to identify small molecules with desirable biological activity and developability. Computational approaches address this challenge by applying in silico evaluators to prioritise compounds for experimental validation. Traditionally, this has been achieved through brute-force virtual screening of large, predefined compound libraries. More recently, advances in generative molecular design (GMD) have enabled models that directly propose molecules optimised for specific objectives, offering favourable computational scaling but often at the expense of progressability, evaluability, and synthetic feasibility. In this review, we analyse molecular search strategies across a spectrum ranging from brute-force search to de novo generative design, focusing on how chemical space is specified and explored. We emphasise three practical axes-computational scaling, progressability, and evaluability-and discuss how different paradigms trade off these properties. In particular, we highlight Generative Virtual Screening (GVS) as a hybrid approach in which generative optimisation is constrained to a fixed, explicit compound library. By combining the computational efficiency of generative methods with the progressability and retrospective evaluability of library-based search, GVS enables scalable exploration of chemical space while providing ground-truth reference points for algorithmic assessment. We argue that GVS offers both immediate practical benefits for hit discovery and a controlled framework for developing and benchmarking generative search algorithms, facilitating their principled extension to broader and less constrained chemical spaces.