For NP-hard problems like Pseudo-Boolean Optimization (PBO), solver performance varies dramatically across instances, making algorithm selection a critical bottleneck. Current Automatic Algorithm Selection (AAS) systems for PBO depend primarily on static problem features, ignoring crucial dynamic information. In contrast, AAS for Boolean Satisfiability (SAT) has demonstrated that “probing” features, gathered from short solver runs, are essential for making high-quality, time-sensitive predictions. This work improves AAS for PBO by systematically integrating and evaluating these powerful probing features. We investigate their impact across a range of machine learning frameworks, including regression, multiclass/multilabel classification, and a novel hybrid model. This investigation culminates in MetaPB, a new open-source meta-solver that combines both static and probing features for superior solver selection. On benchmarks from the 2024 PBO competition, MetaPB outperforms the best individual solver. Remarkably, it closes the gap with leading commercial solvers, achieving performance competitive with Gurobi while using an entirely open-source portfolio. This study establishes a new state of the art for AAS in PBO and challenges the prevailing view in this domain that algorithm selection should be treated purely as a classification task, highlighting the effectiveness of hybrid, regression-informed approaches.