This paper presents the results of an extensive model-scale experimental evaluation of autonomous ship landing guidance and control modes, with flight tests performed in the Maneuvering and Seakeeping (MASK) Basin at the U.S. Naval Surface Warfare Center Carderock Division (NSWCCD). The experiments were performed using a commodities-based multirotor UAV operating from a 20-foot-long model scale ship subject to scaled wave conditions. During testing, two separate guidance algorithms were evaluated: a quadratic programming (QP) based landing algorithm that plans the trajectory to a forecasted deck state, and a simpler “baseline” method that tracks deck motions while closing the distance between the aircraft and deck at a constant rate. Both algorithms commanded a Froude scaled explicit model following control law, and the control laws were used to progressively degrade aircraft tracking bandwidths. The results showed the QP algorithm to be capable of good performance despite poor long term deck state predictions, though several landings with the QP algorithm did terminate with significant velocity and attitude errors. In these cases, however, it is shown that the major factor contributing to degraded performance was not poor deck predictions, giving confidence in the feasibility of incorporating deck predictions directly in path planning. The results also showed that the predictive capabilities of the QP algorithm allowed more direct landing paths to be planned when compared to the baseline algorithm, and also allowed the QP algorithm to land with lower tracking bandwidths. The baseline guidance algorithm, on the other hand, proved to be both simple and reliable when the UAV was in high bandwidth configurations but is sensitive to increased lag in the system.