Ebola virus is a single-stranded, negative-sense RNA virus that causes acute and serious life-threatening illness. In recent years the Ebola virus has spread through several countries in Africa, highlighting the need to develop new treatments for this disease and boosting a new research effort on this subject. However, so far there is no valid treatment for disease created by this pathogen. The Ebola virus Viral Protein 35 (VP35) is a multifunctional protein which is critical for virus replication and infection, and it is considered as a future target for drug development. In this study, we collected 144 VP35 inhibitors which shared the same core scaffold, and a common feature pharmacophore model HypoA was built based on inhibitor-receptor complexes. All 141 compounds were aligned based on the common feature pharmacophore model HypoA (three compounds could not map onto HypoA). The pharmacophore model HypoA was further optimized according to the actual interactions between inhibitors and VP35 protein, resulting in a new pharmacophore model HypoB which was applied for virtual screening. A 3D QSAR model was established by applying the 141 aligned compounds. For the training set, the 3D QSAR model gave a correlation coefficient r2 of 0.897, for the test set, the correlation coefficient r2 was 0.757. Then a virtual screening was carried out, which comprehensively employing the common feature pharmacophore model, 3D QSAR model and docking study, their combination in a hybrid protocol could help to mutually compensate for their limitations and capitalized on their mutual strengths. After the above three virtual screening methods orderly filtering, seven potential inhibitors with novel scaffolds were identified as new VP35 inhibitors. The mapping results of hit compounds onto pharmacophore model and 3D QSAR model, and the molecular interactions of the potential inhibitors with the active site residues have been discussed in detail.