Changes in cell-surface glycan patterns are markers of the presence of many different disease and cancer types, offering a relatively untapped niche for glycan-targeting reagents and therapeutics in diagnosis and treatment. Of paramount importance for the success of any glycan-targeting reagent is the ability to specifically recognize the target among the plethora of different glycans that exist in the human body. The preeminent technique for defining specificity is glycan array screening, in which a glycan-binding protein (GBP) can be simultaneously screened against multiple glycans. Glycan array screening has provided unparalleled insight into GBP specificity, but data interpretation suffers from difficulties in identifying false-negative binding arising from altered glycan presentation, associated with the linker used to conjugate the glycan to the surface. In this work, we model the structure and dynamics of the linkers employed in the glycan arrays developed by the Consortium for Functional Glycomics. The modeling takes into account the physical presence and surface polarity of the array, and provides a structure-based rationalization of false-negative results arising from the so-called “linker effect.” The results also serve as a guide for interpreting glycan array screening data in a biological context; in particular, we show that attempts to employ natural amino acids as linkers may be prone to unexpected artifacts compromising glycan recognition.