Virtual screening has become an indispensable procedure in the drug discovery. Virtual screening methods can be classified into two categories, ligand-based and structure-based methods. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict binding affinity of a ligand in a target binding pocket, albeit with substantially slower speed than ligand-based methods. Here, we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which are represented by 3D Zernike descriptors (3DZDs). By using 3DZDs, shape and physicochemical complementarity of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared to the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by adding two more features, atom-based hydrophobicity and hydrogen bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times faster than conventional structure-based methods. The PL-PatchSurfer2 program are available at kiharalab.org/plps2/.