Lipophilicity is undoubtedly the most widely used and important descriptor in the early stages of drug discovery and development. Additionally, it is a crucial descriptor in substance risk assessment and in areas such as adsorption in materials, catalysts, food chemistry, and computational biology. There are multiple tools to determine this descriptor, mainly for neutral molecules (logPN). For substances with ionizable groups, two formalisms are commonly used to determine the distribution coefficient (logDpH), being the simplest pH correction model is the most widely used. However, previous studies carried out on specific and small molecule sets recommend considering the effect of the apparent ionic compounds (P I app ) because it has a negative impact on the accuracy of computing lipophilic profiles when charged species or related species are ignored. Our study, which was based on a larger amount of data and strictly on experimental values, validated the observations presented in previous studies. We have also evidenced the impact of P I app on the prediction of both the experimental lipophilicity profiles of small molecules and experimental lipophilicity-based applications and metrics such as lipophilic efficiency (LipE), distribution of spiked drugs in milk products, and pH-dependent partition of water contaminants in synthetic passive samples such as silicones. Our findings show that better predictions are obtained by considering the apparent ionic partition, whereas ignoring its contribution can lead to inadequate experimental simplifications and/or computational predictions.