Chemical structures come in various shapes and sizes, depending on the scientists or even algorithms that create them. Though variabilities may sometimes seem subtle to a trained chemist’s eyes, these can introduce inconsistencies that impair chemical search algorithms or model building. Structure normalization is a key component of any cheminformatics workflow with an often underestimated significance. Finding relationships between chemical structures and their measured properties primarily relies on the representation of the chemical matter. Variability of the calculated features and descriptors for these representations can influence data analysis and accuracy of the predictions. The goal of the presentation is to highlight the effect of chemical normalization on investigating correlations and building predictive models.