This paper proposes a novel neural network ansatz for solving the electronic Schrödinger equation using Variational Monte Carlo (VMC) methods. The proposed ansatz maps uncorrelated, computationally cheap Hartree-Fock orbitals to correlated, high-accuracy neural network orbitals, enabling the learning of a single wavefunction across multiple compounds and geometries. The authors demonstrate the transferability of the wavefunction model by pre-training it on smaller fragments and successfully applying it to larger compounds. They show that extensive pre-training of such a generalized wavefunction model across different compounds and geometries can lead to a foundation wavefunction model that yields high-accuracy ab-initio energies with minimal computational effort for fine-tuning and evaluation of observables. The method is evaluated on various tasks, including predicting relative energies, and compared with other state-of-the-art methods, showing superior performance in terms of accuracy and efficiency. The authors also discuss the scalability of their approach and highlight future directions for improving accuracy and computational efficiency.This paper proposes a novel neural network ansatz for solving the electronic Schrödinger equation using Variational Monte Carlo (VMC) methods. The proposed ansatz maps uncorrelated, computationally cheap Hartree-Fock orbitals to correlated, high-accuracy neural network orbitals, enabling the learning of a single wavefunction across multiple compounds and geometries. The authors demonstrate the transferability of the wavefunction model by pre-training it on smaller fragments and successfully applying it to larger compounds. They show that extensive pre-training of such a generalized wavefunction model across different compounds and geometries can lead to a foundation wavefunction model that yields high-accuracy ab-initio energies with minimal computational effort for fine-tuning and evaluation of observables. The method is evaluated on various tasks, including predicting relative energies, and compared with other state-of-the-art methods, showing superior performance in terms of accuracy and efficiency. The authors also discuss the scalability of their approach and highlight future directions for improving accuracy and computational efficiency.