Background: Respiratory syncytial virus (RSV) RNA-dependent RNA polymerase (RdRp) complex is an essential molecular machine for viral genome replication. The L protein, the catalytic subunit of this complex (L-RdRp), is well-characterized structurally and represents a highly promising target for the development of novel small-molecule drugs against RSV. Methods: To address the limitations of current QSAR-based virtual screening strategies for RSV L-RdRp inhibitor development, we established a multi-dimensional computer-aided drug screening framework integrating activity, toxicity, drug-likeness, and stability. Results: Two OECD-compliant 2D-QSAR models were developed and rigorously validated to predict inhibitory activity and cytotoxicity, respectively. The optimal inhibitory activity model exhibited strong statistical performance, with R² = 0.8281, $Q_{\mathrm{L}\mathrm{O}\mathrm{O}}^2=$ 0.7653, $R_{\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}}^2=$ 0.8713, $Q_{\mathrm{F}\mathrm{n}}^2=$ 0.8594 ∼ 0.8837, CCC_test = 0.9301, MAE_test = 0.1966. Similarly, the best cytotoxicity model achieved R²= 0.8263, $Q_{\mathrm{L}\mathrm{O}\mathrm{O}}^2$ = 0.7422, $R_{\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}}^2$ = 0.8951, $Q_{\mathrm{F}\mathrm{n}}^2$ = 0.8108~0.8530, CCC_test = 0.9081, MAE_test = 0.1685. Based on these models, a four-step screening workflow—QSAR-based filtering and molecular docking (15,758 → 2446 → 162 → 19 compounds), ADMET evaluation (19 → 5), and molecular dynamics simulations (MDSs)—was implemented to identify promising L-RdRp inhibitors. Conclusions: Ultimately, five candidate compounds were selected, all of which demonstrated predicted higher inhibitory activity, lower predicted cytotoxicity, a stable predicted binding mode, and favorable oral bioavailability compared with the reference drug remdesivir. These findings provide valuable in silico-derived lead candidates and a reliable computational workflow for identifying experimental L-RdRp inhibitors targeting RSV.