Selecting suitable glucose binding proteins (GBPs) is vital for biosensor development for medical diagnostics and quality control in food industry. Biosensors offer advantages such as high specificity, selectivity, fast response time, continuous measurement, and cost- effectiveness. The current work utilized a combination of molecular docking, molecular dynamics (MD) simulations and free energy calculations to develop a high-throughput bioinformatics pipeline to select GBP candidates from an extensive protein database. GBPs with good binding affinity to glucose were virtually screened from Protein Data Bank using molecular docking. MD simulations ascertained the binding dynamics of a few selected candidates. Further, steered MD (Brownian dynamics fluctuation-dissipation-theorem) was used to estimate binding free energies of the ligand-protein complex. Correlations between ligand-binding parameters obtained from relatively longer MD simulations and binding parameters interpreted from faster docking simulations were investigated. The correlation plots suggested that, a combination of threshold values of the following three docking parameters; docking binding energy, binding cavity depth and the number of hydrogen bonds between the ligand and binding site residues can be used to reliably predict candidate GBPs. Thus, a high- throughput and accurate protein selection process based on relatively faster docking simulations was proposed to screen GBPs for glucose biosensing.