The increased automatization of force field parameter development with reduced human involvement underscores the importance of the quality and proper interpretation of target data. Optimal experimental target data have high accuracy and direct connection with simulations. Instead, data requiring approximate models to connect to physical properties should be relegated to an inferior role despite being easier for humans to interpret. For example, for lipid bilayers, the intuitively intelligible area per lipid can be determined only indirectly from experiments, and with relatively low accuracy. In contrast, the C-H bond order parameters from NMR have a more direct connection with experiments and higher accuracy, thereby being better quantities to evaluate MD simulation quality. However, care must be taken also when using order parameters as target data. For example, the lack of information on their sign or forking (two C-H bonds at the same carbon having different order parameters) can misguide the fitting algorithm to incorrect values, leading to significant deviation of the conformational ensemble from experiment, see Fig. . A similar situation may also appear if interfaces with incorrect surface pressures are used as fitting targets. Nevertheless, the game-changing advantage of automated parameter optimization in such cases is the ease of repeating the fitting procedure with better target parameters when they become available.