Cell surface glycans are involved in various physiological processes including cell-cell recognition, cell signaling, host immune response, and host-pathogen interaction during infection. Aberrant glycosylation is commonly seen in many different diseases, perhaps most notably in cancer. This can serve as a biomarker for disease, thus requiring development of reagents that can bind specifically to these glycans. The ability of antibodies to bind to various biomolecules with high specificity, combined with the advances in antibody engineering methods, makes them attractive diagnostic and therapeutic reagents. However, anti-carbohydrate antibodies generated against carbohydrate antigens often bind with low affinities. Additionally, cross reactivity is often observed with anti-carbohydrate antibodies due to the structural similarity of glycans. Therefore, antibody optimization is an essential step for the development of high affinity antibodies. A 3D structure of the antibody-carbohydrate complex can provide structural insight into the origin of affinities and specificities, however, 3D structures are often unavailable. Here, we employed a series of computational techniques to study two antibody systems: anti-blood group A antibody (BGA) and CS-35 antibody-furanose system. In the BGA study, results indicated that while both antigen A and B can be accommodated in the antibody combining site, antigen A is preferred by 4 kcal/mol. In the CS-35 study, calculated binding energies played a crucial role in the rational design of mutants. In both these studies, detailed insights into the origin of affinity and specificity were obtained through integration of computation and experimental results; computational predictions were validated using surface plasma resonance (SPR) and bio-layer interferometry binding experiments (BLI). To facilitate a standardized method for modeling antibody-carbohydrate complexes, a protocol involving molecular docking and molecular dynamics was developed and tested on 14 antibody-carbohydrate complexes. This resulted in a protocol amenable to automation for modeling antibody-carbohydrate complexes.