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Ferromagnetic resonance (FMR) is a broadly used dynamical measurement used to characterize a wide range of magnetic materials. Applied research and development on magnetic thin film materials is growing rapidly alongside a growing commercial appetite for magnetic memory and computing technologies. The ability to execute high-quality, fast FMR surveys of magnetic thin films is needed to meet the demanding throughput associated with rapid materials exploration and quality control. Here, we implement optimal Bayesian experimental design software developed by [McMichael et al. J. Res. Natl. Inst. Stand. Technol. 126, 126002 (2021)] in a vector network analyzer-FMR setup to demonstrate an unexplored opportunity to accelerate FMR measurements. A systematic comparison is made between the optimal Bayesian measurement and the conventional measurement. Reduced uncertainties in the linewidth and resonance frequency ranging from 40% to 60% are achieved with the Bayesian implementation for the same measurement duration. In practical terms, this approach reaches a target uncertainty of ±5 MHz for the linewidth and ±1 MHz for the resonance frequency in 2.5× less time than the conventional approach. As the optimal Bayesian approach only decreases random errors, we evaluate how large systematic errors may limit the full advantage of the optimal Bayesian approach. This approach can be used to deliver gains in measurement speed by a factor of 3 or more and as a software add-on has the flexibility to be added on to any FMR measurement system to accelerate materials discovery and quality control measurements, alike.