The tunable optical response of plasmonic nanoparticle assemblies makes them attractive for applications including sensing, 1,2 energy conversion, and theranostics. Developing and employing methods for electrodynamics simulation of metallic nanoparticle assemblies are essential for advancing conceptual understanding and design. The resonant interaction of free charge carriers in nanoparticles with light (localized surface plasmon resonance, LSPR) results in strong electromagnetic near-fields and enhanced absorption and scattering. Due to LSPR coupling, the spectral response of plasmonic assemblies is sensitive to the nanoparticle spatial arrangement, i.e., structure, with amplified electromagnetic fields or hot spots emerging in the gaps between closely spaced nanoparticles. Analytical approaches such as Mie theories or plasmon hybridization theory and numerical solution of the classical electromagnetic scattering equations have clarified the influence of nanoparticle morphology and coupling in small plasmonic clusters, oligomers, and periodic arrays. However, an open question is how to compute the optical response of significantly more complex assemblies with characteristic structural features involving large numbers of nanoparticles, such as compositionally disordered superlattices or gel networks. Structural complexity of extended, disordered nanoparticle assemblies poses challenges for conventional electromagnetic simulation techniques. Discretization requirements for the most flexible approaches-discrete dipole approximation, finite difference time domain, finite element, and boundary element methods -render them too computationally demanding to simulate configurations with a large number N (> 10 3 ) of nanoparticles. This shortcoming can be avoided for assemblies of spherical nanoparticles using strategies like the multisphere T-matrix method, which is based on generalized Mie theory and does not require spatio-temporal discretization. However, even these approaches become slow to converge or fail altogether when nanoparticle pairs touch or overlap, which inevitably occurs in large configurations generated from simulations of assembly using coarse-grained models. The ability to predict the optical response of complex assemblies, including those from simulated nanoparticle configurations, is needed to match the complexity of emerging experimental systems and accelerate their development.