Molecular packing is often complicated by dense phases, irregular molecular geometries, or multi-component systems. These complexities make the problem of packing molecules substantially harder than the classical mathematical problem of sphere packing . Furthermore, the use of explicit solvents introduces the need to balance computational efficiency with physical accuracy when placing molecules in the box. For example, packing small pseudo-spherical solvent molecules can be done very efficiently by placing molecules on a grid and removing the overlaps with the solute, but if more complicated shapes are considered, this strategy leads to a low density packing. Various strategies with different level of sophistication have been developed to address this challenge. On the most sophisticated end is Packmol, a widely used tool that generates dense packings by iteratively minimizing a cost function subject to constraints on interatomic distances . This approach efficiently minimizes steric clashes and ensures uniform distribution, making it ideal for generating high-quality initial configurations. However, Packmol requires user-defined constraints and can be computationally expensive for very large systems or complex mixtures. At the opposite end, programs like GROMACS employs a simple yet very efficient strategy: using pre-equilibrated solvent boxes. The method involves replicating a small, pre-equilibrated box of solvent in three dimensions to fill the target simulation volume and then removing the solvent molecules that overlaps with the solute. While this approach is extremely fast and widely used, it relies on the availability of pre-equilibrated boxes for specific solvents. Moreover, it offers limited flexibility for uncommon solvents, novel mixtures, or highly irregular geometries since the problem of the initial packing remains and only benefits of large size scaling. The random insertion or bruteforce approach is a straightforward method in which each solvent molecule is placed at a random position and orientation in the simulation box. This strategy is highly generalizable and does not require pre-equilibrated solvent boxes, user-defined constraints, or complex algorithms. It is particularly useful for solvating uncommon solvents, ionic liquids, or custom molecular mixtures. However, the main limitation of brute-force packing is inefficiency: as the box becomes crowded, the overlap rejection rate increases significantly, leading to wasted computational attempts. This limitation becomes especially pronounced for dense systems, where naive overlap checking scales poorly with the system size. A key to improving random insertion is accelerating overlap detection. Naive overlap checks involve comparing the distances between the newly inserted molecule and all pre-existing molecules, resulting in O(N ) complexity per insertion, where N is the total number of atoms in the box. This yields an overall complexity of O(N 2 ) for packing the system. The inefficiency of this approach has historically limited the practicality of brute-force packing for large systems.