Often, eye-tracking researchers define areas of interest (AOIs) to analyze eye-tracking data. Although AOIs can be defined with systematic methods, researchers in organic chemistry education eye-tracking research often define them manually, as the semantic composition of the stimulus must be considered. Still, defining appropriate AOIs during data preparation is crucial, as it can impact AOI-based metrics-even to the extent of their significance. However, defining AOIs can be challenging when dealing with complex and dense stimuli, such as organic representations. To address this challenge, we introduce an already established systematic method, the limited-radius Voronoi tessellation (LRVT) method, which has not yet been used in chemistry education research. While being algorithmic, this method still considers the semantic composition of the stimulus and could offer an alternative to manually defining AOIs in organic representations. By applying the LRVT method, the position of atoms can be used to construct AOIs that represent the closest area to these atoms within a defined radius. In this communication, we illustrate the how-to of constructing AOIs with the LRVT method during data preparation and demonstrate how these AOIs can be used to analyze and compare visual problem-solving behavior in different grain sizes through exemplary analyses. Finally, we discuss the advantages and limitations of this alternative method.