Identifying the novel and potential starting lead compounds remains major challenge in drug discovery industry. In 90’s, high-throughput screening is a common practice in early stage of a project; screening of large number compounds to identify potential starting lead compounds which can modulate target of their interest. Although high-throughput screening has some remarkable successes in identifying potential drug leads, still HTVS is not fruitful as expected, due to the costly nature of chemicals, assays and also depends on the chemical space of the input library [1-4]. Over the years, tremendous research has carried out towards the designing of virtual libraries and virtual screening to identifying the potential starting leads, to reduce the cost of High throughput Screening and to improve the success rate. In current issue of “Current Topics in Medicinal Chemistry (CTMC)”, we focused on the recent trends in library design, Denovo studies, structure and ligand-based virtual screening approaches towards the medicinal chemistry and drug discovery topics and entitled the special topic with “Recent Trends in Library Design and Virtual Screening in Medicinal Chemistry and Drug Discovery”. Articles in this special issue have been contributed by experts from many parts of the world. In the first article, Drs. Rahul and Se Won Park discussed about discovery of a tuberculosis drug: bedaquiline, its anti-tuberculosis effects, mode of action and also discussed about computer-aided drug design approach to predict the binding mode for bedaquiline. In the next article, Drs. Prema Latha & Thomas discussed about successful application of structure-based drug design of Zanamivir (Relenza™) and oseltamivir (Tamiflu®), antiviral drugs. Also, discussed about application of computer-aided methods in identifying leads against influenza targets. Next article contributed by Drs. Harikishore & Yoon discusses about recent developments in denovo drug design and also explained successful application of the ligand and receptor based de novo drug design approaches. In the next article, Drs. Rodolpho & Carolina, summarizes the recent developments in virtual screening strategies and also highlights the recent achievements and as well as challenges. Also, discussed recent example of successful application for the identification of novel hit compounds for Trypanosoma cruzi sterol 14 α-demethylase (CYP51). In another article, Drs. Dimitar, Girinath, & Mati has covered a variety of studies have demonstrated the potential of machine- learning methods for predicting compounds as potential drug candidates. Also, provided an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. In next article, Drs. Evanthia & Zoe Cournia has covered an overview to the principles and applications of Structure-based Virtual Screening (VS) approaches and discussed on recent trends in library design, and as well as discuss limitations of the method.