Histone deacetylase (HDAC) inhibitors have gained attention over the past three decades because of their potential in the treatment of different diseases including various forms of cancers, neurodegenerative disorders, autoimmune, inflammatory diseases, and other metabolic disorders. To date, 5 HDAC inhibitor drugs are marketed for the treatment of hematological malignancies and several drug-candidate HDAC inhibitors are at different stages of clinical trials. However, due to the toxic side effects of these drugs resulting from the lack of target selectivity, active studies are ongoing to design and develop either class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure–activity relationships (3D-QSAR); and structure-based virtual screening (molecular docking). The current trends involve the application of the combination of these methods and incorporating molecular dynamics simulations coupled with Poisson–Boltzmann/molecular mechanics generalized Born surface area (MM-PBSA/MM-GBSA) to improve the prediction of ligand binding affinity. This review aimed at understanding the current trends in applying these multilayered strategies and their contribution to the design/identification of HDAC inhibitors.