Coronavirus disease 2019 (COVID-19) is transitioning from a pandemic to an endemic phase through recurring mutations. Initial efforts focused on developing strategies to mitigate infection of lung epithelial cells which are the primary targets of the SARS-CoV-2 virus using the affinity of the spike protein to human ACE2 receptor. SARS-CoV-2, however, infects additional cell types present in the lung such as macrophages through the alternate entry receptor Neuropilin 1 (NRP1). Developing novel therapeutic strategies to prevent SARS-CoV-2 infection of cells crucial for immunosurveillance could thus be integral to treat post-acute sequelae of COVID-19 (PASC). Since traditional drug development process takes a long time, it is imperative to establish new strategies that can be rapidly deployed to combat the dynamic nature of COVID-19 evolution and to contribute to prevention of future pandemics. We obtained the gene expression profiles of THP-1 monocytes from L1000-based Connectivity Map using CLUE, cloud- based software platform for the analysis of perturbational datasets to identify compounds that could reduce the expression level of NRP1. Out of 33,590 compounds, we analyzed the profiles of 45 compounds for their ability to reduce NRP1 expression. We selected the top five small molecule inhibitors predicted to decrease the expression of NRP1 for validation studies. All five selected compounds showed low cytotoxicity at tested doses and their ability to reduce NRP1 surface expression was evaluated in THP-1 monocytes, THP-1-derived macrophage like cells and human peripheral blood mononuclear cell (PBMC)-derived primary macrophages. Five compounds with the largest predicted reduction of NRP1 expression decreased macrophage NRP1 surface expression measured using flow cytometry and fluorescent microscopy assays in both cell line and primary macrophages. Using our computational approach, we identified 45 compounds that could potentially decrease NRP1 surface expression in macrophages based on their effect on THP-1 monocytes. Validation studies showed that such an approach can help to identify compounds for drug repositioning in target cells that are absent in the L1000 database. Our proposed approach can be applicable for the rapid compound exploration to combat novel cell types that SARS-CoV-2 targets for infection and could provide molecular bases for the development of new drugs.