The current study deciphers the combined ligand- and structure-based computational insights to profile structural determinants for the selectivity of representative diverse classes of FXa-selective and thrombin-selective as well as dual FXa-thrombin high affinity inhibitors. The thrombin-exclusive insertion 60-loop (D-pocket) was observed to be one of the most notable recognition sites for the known thrombin-selective inhibitors. Based on the topological comparison of four common active-site pockets (S1-S4) of FXa and thrombin, the greater structural disparity was observed in the S4-pocket, which was more symmetrical (U-shaped) in FXa as compared to thrombin mainly due to the presence of L99 and I174 residues in latter in place of Y99 and F174 respectively in former protease. The S2 pocket forming partial roof at the entry of 12 Å deep S1-pocket, with two extended β-sheets running antiparallel to each other by undergoing U-turn (∼180̊), has two conserved glycine residues forming H-bonds with the bound ligand for governing ligand binding affinity. The docking, scoring, and binding pose comparison of the representative high-affinity and selective inhibitors into the active sites of FXa and thrombin revealed critical residues (S214, Y99, W60D) mediating selectivity through direct- and long-range electrostatic interactions. Interestingly, most of the thrombin-selective inhibitors attained S-shaped conformation in thrombin, while FXa-selective inhibitors attained L-shaped conformations in FXa. The role of residue at 99th position of FXa and thrombin toward governing protease selectivity was further substantiated using molecular dynamics simulations on the wild-type and mutated Y99L FXa bound to thrombin-selective inhibitor 2. Furthermore, predictive CoMFA (FXa q² = 0.814; thrombin q² = 0.667) and CoMSIA (FXa q² = 0.807; thrombin q² = 0.624) models were developed and validated (FXa r²(test) = 0.823; thrombin r(2)(test) = 0.816) to feature molecular determinants of ligand binding affinity using the docking-based conformational alignments (DBCA) of 141 (88(train)+53(test)) and 39 (27(train)+11(test)) nonamidine class of potent FXa (0.004 ≤ K(i) (nM) ≤ 4700) and thrombin (0.001 ≤ K(i) (nM) ≤ 940) inhibitors, respectively. Interestingly, the ligand-based insights well corroborated with the structure-based insights in terms of the role of steric, electrostatic, and hydrophobic parameters for governing the selectivity for the two proteases. The new computational insights presented in this study are expected to be valuable for understanding and designing potent and selective antithrombotic agents.