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An in-depth assessment of the High Lift-Common Research Model (HL-CRM) aerodynamic performance is made using a Reynolds-Averaged Navier-Stokes (RANS) methodology. Results presented here were produced in support of the 4th AIAA High Lift Prediction Workshop(HLPW-4), with additional simulation results and analysis used to support findings from the workshop. Mesh resolution, RANS closure models, and tunnel effects were among the sources of simulation sensitivity addressed, all in the pursuit of evaluating best-practice simulation methods for complex high-lift configurations. In general, free-air simulations using RANS yield accurate predictions of integrated loads in the pre-stall regime for the HL-CRM, while strong sensitivity to simulation method is observed near CLmax. Additionally, a study assessing changes in aerodynamic performance due to flap deflection angle indicated strong sensitivity to turbulence model closure and mesh resolution, particularly at the highest deflection setting. Several available corrections for the Spalart-Allmaras (SA) turbulence model were tested and generally introduced more error relative to the experiment in comparison to the baseline SA closure model. The Standard Menter Baseline Two-Equation (k-ωBSL) turbulence model was also explored, which arguably yielded results highly consistent with experiment, but at a substantially higher computational cost. Therefore, as a compromise between computational cost and accuracy, the baseline SA closure model was selected for additional HL-CRM studies that assess alternative solution methods. These strategies include “warm start” approaches, which initialize the simulation from a previously converged solution at a nearby angle of attack, unsteady calculations, and simulations that model the experimental wind tunnel. Results from these studies would reinforce the shortcomings of RANS in predicting aircraft performance at high-lift conditions, namely at high-αand highly loaded flap conditions, while also answering questions about appropriate modeling techniques to minimize the errors associated with RANS where possible