Laser Powder Bed Fusion (LPBF) of metals offers immense potential for additive manufacturing, providing nearly limitless geometric design freedom and the capability to tailor local material properties. However, the process is governed by a multitude of complex physical mechanisms spanning several length scales. Suboptimal process parameters can lead to severe defects, significantly compromising part integrity. Furthermore, current production rates remain low, limiting LPBF to high-value components rather than mass production. Consequently, predictive simulation models are essential to enhance physical understanding and enable the exploration of novel process strategies that achieve higher build rates while ensuring controlled part quality. This presentation introduces a comprehensive computational modeling framework that captures the diverse length scales and process steps of LPBF to enable the simulation-based exploration of novel, high-throughput process strategies. It consists of sub-models for cohesive powder mechanics, melt pool dynamics - including rapid evaporation and gas/vapor-powder interactions - as well as part-scale predictions of temperature, microstructure, and residual stress. Beyond the physical models – including a novel, pressure-aware formulation for evaporative mass flux and recoil pressure – the talk addresses efficient code implementation as a prerequisite for simulations on practically relevant scales. Eventually, concrete application examples highlighting simulation-driven research and experimental validation are presented. First, innovative strategies for the powder deposition process are discussed, facilitating the robust and uniform spreading of highly cohesive powders. Second, novel strategies for the LPBF process itself are examined, such as the targeted adjustment of atmospheric pressure or layer thickness. These approaches ultimately aim to significantly increase build rates while maintaining process stability.