In the pursuit of novel drug discovery which is known to be a lengthy process, it is pivotal to assess drug-likeness to filter out compounds with unfavourable properties early in the development process. Recent advances in computational methodologies, coupled with the exponential growth of available chemical data and improvements in machine learning algorithms and emerging AI, have revolutionized drug-likeness screening. This review explores the latest developments in drug-likeness screening, focusing on the utilization of software and online tools. We discuss the uses and principles behind various computational methods employed for drug-likeness prediction, including molecular docking, quantitative structure-activity relationship (QSAR) modelling, and machine learning approaches. Additionally, the emergence of user-friendly online platforms and databases offering accessible tools for drug-likeness evaluation, democratizing the process for researchers across different domains has been highlighted. Furthermore, the possible challenges and opportunities associated with integrating these computational tools into drug discovery pipelines and the importance of validation, interpretability, and integration with experimental data is emphasized. Overall, this review provides insights into the evolving landscape of drug likeness screening and its integration with computational tools, facilitating more efficient and cost-effective drug discovery endeavours.