Classical simulation of molecular systems is fundamentally limited by exponential scaling, motivating the development of quantum algorithms such as the Variational Quantum Eigensolver (VQE). While the Adaptive VQE (ADAPT-VQE) improves upon VQE by dynamically constructing ansätze from an operator pool, its one-operator-at-a-time strategy can lead to high computational overhead. In this work, we introduce K-ADAPT-VQE, a generalization of ADAPT-VQE that selects operators in chunks of K per iteration, thereby accelerating convergence. Using the MIMIQ quantum simulator, we benchmark the method on \(\textrm{BeH}_2\) , LiH, and \(\textrm{N}_2\) , demonstrating that K-ADAPT-VQE consistently reduces the number of iterations and quantum function calls required to achieve chemical accuracy, with improvements of up to an order of magnitude relative to ADAPT-VQE. These findings indicate that chunked operator selection offers a scalable path for applying adaptive ansätze construction to increasingly complex molecular systems.