This paper combines stochastic resetting with Metadynamics (MetaD) to enhance the efficiency of molecular dynamics (MD) simulations. MetaD is a powerful method for accelerating MD simulations by identifying collective variables (CVs) that capture slow modes of the process. However, finding optimal CVs can be challenging. The authors present a CV-free approach to enhanced sampling using stochastic resetting, which involves periodically restarting simulations with new initial conditions. By combining these two methods, they demonstrate that greater acceleration can be achieved compared to using either method alone. They also show that applying stochastic resetting to MetaD simulations with suboptimal CVs can lead to speedups comparable to those obtained with optimal CVs. This suggests that stochastic resetting can serve as an alternative to the difficult task of improving suboptimal CVs. The paper further proposes a method to extract unbiased mean first-passage times from MetaD simulations with resetting, improving the trade-off between speedup and accuracy. The results are validated through simulations of a two-well model, a modified Faradjian-Elber potential, alanine tetrapeptide, and chignolin in explicit water. The authors conclude that the combination of stochastic resetting and MetaD is a promising approach for accelerating a broad range of molecular simulations.This paper combines stochastic resetting with Metadynamics (MetaD) to enhance the efficiency of molecular dynamics (MD) simulations. MetaD is a powerful method for accelerating MD simulations by identifying collective variables (CVs) that capture slow modes of the process. However, finding optimal CVs can be challenging. The authors present a CV-free approach to enhanced sampling using stochastic resetting, which involves periodically restarting simulations with new initial conditions. By combining these two methods, they demonstrate that greater acceleration can be achieved compared to using either method alone. They also show that applying stochastic resetting to MetaD simulations with suboptimal CVs can lead to speedups comparable to those obtained with optimal CVs. This suggests that stochastic resetting can serve as an alternative to the difficult task of improving suboptimal CVs. The paper further proposes a method to extract unbiased mean first-passage times from MetaD simulations with resetting, improving the trade-off between speedup and accuracy. The results are validated through simulations of a two-well model, a modified Faradjian-Elber potential, alanine tetrapeptide, and chignolin in explicit water. The authors conclude that the combination of stochastic resetting and MetaD is a promising approach for accelerating a broad range of molecular simulations.