We introduced Vendi sampling, a replica-based sampling method for molecular simulations. Vendi sampling overcomes large free energy barriers and enhances sampling. While there is a wide variety of enhanced sampling techniques available in the literature , Vendi sampling, to our best knowledge, takes a unique approach to the problem. It is driven by a diversity metric, the Vendi score, which is computed according to a kernel matrix that performs pairwise comparisons of the states of all replicas. Consequently, the system of coupled replicas defined by Vendi sampling forms an extended ensemble that is neither driven by the choice or modulation of a macroscopic thermodynamic parameter (e.g. temperature or Hamiltonian) nor by the definition of a collective variable. Instead, the extended ensemble is defined by a kernel function. The kernel function computes inner products in feature spaces, e.g. high-dimensional collective variables. However, since we do not need to explicitly compute the features in the kernel formalism, the underlying features can, in principle, be infinite-dimensional. We can thereby avoid costly collective variable identification, without suffering from 'the curse of dimensionality.'