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Network pharmacology studies how a drug acts on networks of biological targets rather than on a single target, and it has been welcomed as a way to explain the multi-component, multi-target action of Ayurvedic drugs and formulations. Free databases and web tools now allow a complete study to be performed in silico within weeks, and publications applying a near-identical pipeline to Ayurvedic plants have multiplied. This review critically appraises each step of that pipeline as it is commonly applied in Dravyaguna research: compiling constituents, filtering them by predicted absorption and drug-likeness, predicting targets, building protein interaction networks, performing enrichment analysis and molecular docking, and validating predictions. At each step we identify recurrent problems. Compound lists describe what a plant has ever been reported to contain rather than what a classical preparation delivers; absorption filters rely on predicted values and arbitrary thresholds; promiscuous compounds such as curcumin and well-studied proteins dominate networks irrespective of the disease studied; enrichment results are often generic; docking scores are treated as evidence of activity; and experimental validation is frequently absent or uses irrelevant doses. We summarise the recommendations of the 2021 international evaluation guidance and of best-practice standards for phytopharmacological research, and propose a 12-item minimum standard for network pharmacology studies of Ayurvedic drugs. Used with chemically characterised classical preparations, transparent reporting and experimental or clinical validation, network pharmacology can test Dravyaguna hypotheses rather than merely decorate them.