Air-to-refrigerant heat exchangers (HXs) have been the topic of considerable research efforts since they are fundamental components of Heating, Ventilation, Air-Conditioning, & Refrigeration (HVAC&R) systems. Recent advancements in simulation software (e.g., Computational Fluid Dynamics (CFD)) and optimization algorithms have enabled primary tube shape and topology optimization to design highly compact HXs with small tube characteristic diameters which are capable of outperforming current state-of-the-art fin-and-tube and microchannel HXs. In HVAC&R systems, many HXs operate under dehumidifying (wet) conditions (e.g., evaporator coils), which is a complex, simultaneous heat and mass transfer process. From a simulation perspective, the dehumidification mass transfer coefficient is typically computed by applying the Reynolds and Chilton-Colburn analogies, which approximates the Lewis number as unity to simplify the analysis. However, this assumption is not entirely satisfied for typical air-conditioning operating ranges (i.e., completely dry to saturated air from 10°C to 60°C), where the Lewis number ranges from 0.81 − 0.86, thus potentially compromising the prediction accuracy for HX performance under wet operating conditions. In this paper, a CFD model is developed to predict the dehumidification performance of novel finless tube bundles which feature small diameter, non-round, shape-optimized tubes. The model is utilized to develop Lewis number correlations as a function of HX geometry and inlet air operating conditions. CFD model and correlation validation were conducted through comparisons with experimental data from a conventionally-manufactured, finless HX with non-round, shape-optimized tubes. The CFD-based Lewis number correlations predicted 91% of experimental Lewis numbers within ±30%. The acceptable agreement between simulation and experimental results for the present application highlights the flexibility of the new correlations to improve the HX performance prediction accuracy under wet operating conditions.