We have employed machine learning approaches to virtually screen the large CHEMBL compound database consisting of nearly 2 million compounds. The convolutional networks on graphs (GraphConv) model show the best performance on the test set with Pearson's R correlation of 0.8 and RMSE of 1.86 kcal/mol. Molecular docking and MD simulations were employed to understand structural insights into the binding process between top compounds and neuraminidase. The MD simulations shed light into side chain and hydrogen bond contacts between the top compound and neuraminidase and indicate important residues which stabilize the protein-ligand interaction. Experimental investigations on the enzymatic inhibition and antiviral activities as well as cytotoxicity of 11 compounds randomly selected from the top 400 compounds indicated that the drug screening hit rate was elevated to an impressive range of 9.1% to 18.2% when informed by virtual screening predictions. This is starkly contrasting the less than 1% hit rates achieved by conventional high-throughput screening methods. Such findings underscore the immense value of virtual screening in expediting the identification of potential drug candidates with IAV-inhibitory properties. However, we acknowledge that further refinements are essential in our ongoing pursuit. While these compounds exhibit promise in NA inhibition, their efficacy in the cellbased assay was not good compared to the control group. This observation suggests the virtual screening process may only encompass some relevant factors on the virus's surface. As we chart our research path forward, we recognize the need for strategic compound modifications to enhance their NA inhibition capacity. Future investigations will be dedicated to fine-tuning these compounds to elevate their antiviral potential and advance the fight against IAV.